Overview

Dataset statistics

Number of variables49
Number of observations268850
Missing cells2539905
Missing cells (%)19.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory89.7 MiB
Average record size in memory350.0 B

Variable types

Categorical13
Numeric21
Boolean6
Text9

Alerts

telekomHybridUploadSpeed has constant value ""Constant
telekomTvOffer is highly imbalanced (83.5%)Imbalance
newlyConst is highly imbalanced (60.2%)Imbalance
electricityBasePrice is highly imbalanced (58.0%)Imbalance
serviceCharge has 6909 (2.6%) missing valuesMissing
heatingType has 44856 (16.7%) missing valuesMissing
telekomTvOffer has 32619 (12.1%) missing valuesMissing
telekomHybridUploadSpeed has 223830 (83.3%) missing valuesMissing
telekomUploadSpeed has 33358 (12.4%) missing valuesMissing
totalRent has 40517 (15.1%) missing valuesMissing
yearConstructed has 57045 (21.2%) missing valuesMissing
noParkSpaces has 175798 (65.4%) missing valuesMissing
firingTypes has 56964 (21.2%) missing valuesMissing
yearConstructedRange has 57045 (21.2%) missing valuesMissing
houseNumber has 71018 (26.4%) missing valuesMissing
condition has 68489 (25.5%) missing valuesMissing
interiorQual has 112665 (41.9%) missing valuesMissing
petsAllowed has 114573 (42.6%) missing valuesMissing
streetPlain has 71013 (26.4%) missing valuesMissing
typeOfFlat has 36614 (13.6%) missing valuesMissing
thermalChar has 106506 (39.6%) missing valuesMissing
floor has 51309 (19.1%) missing valuesMissing
numberOfFloors has 97732 (36.4%) missing valuesMissing
description has 19747 (7.3%) missing valuesMissing
facilities has 52924 (19.7%) missing valuesMissing
heatingCosts has 183332 (68.2%) missing valuesMissing
energyEfficiencyClass has 191063 (71.1%) missing valuesMissing
lastRefurbish has 188139 (70.0%) missing valuesMissing
electricityBasePrice has 222004 (82.6%) missing valuesMissing
electricityKwhPrice has 222004 (82.6%) missing valuesMissing
serviceCharge is highly skewed (γ1 = 409.1643225)Skewed
totalRent is highly skewed (γ1 = 466.5675418)Skewed
noParkSpaces is highly skewed (γ1 = 210.5968365)Skewed
baseRent is highly skewed (γ1 = 500.2019466)Skewed
livingSpace is highly skewed (γ1 = 373.8392602)Skewed
noRooms is highly skewed (γ1 = 249.536282)Skewed
floor is highly skewed (γ1 = 155.4380567)Skewed
numberOfFloors is highly skewed (γ1 = 114.0924256)Skewed
heatingCosts is highly skewed (γ1 = 63.88736061)Skewed
lastRefurbish is highly skewed (γ1 = 24.62432609)Skewed
scoutId has unique valuesUnique
serviceCharge has 3434 (1.3%) zerosZeros
picturecount has 4971 (1.8%) zerosZeros
pricetrend has 11082 (4.1%) zerosZeros
noParkSpaces has 3855 (1.4%) zerosZeros
floor has 24604 (9.2%) zerosZeros
heatingCosts has 2754 (1.0%) zerosZeros

Reproduction

Analysis started2024-04-03 18:23:20.995005
Analysis finished2024-04-03 18:29:53.331912
Duration6 minutes and 32.34 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

regio1
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Nordrhein_Westfalen
62863 
Sachsen
58154 
Bayern
21609 
Sachsen_Anhalt
20124 
Hessen
17845 
Other values (11)
88255 

Length

Max length22
Median length18
Mean length12.165297
Min length6

Characters and Unicode

Total characters3270640
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNordrhein_Westfalen
2nd rowRheinland_Pfalz
3rd rowSachsen
4th rowSachsen
5th rowBremen

Common Values

ValueCountFrequency (%)
Nordrhein_Westfalen 62863
23.4%
Sachsen 58154
21.6%
Bayern 21609
 
8.0%
Sachsen_Anhalt 20124
 
7.5%
Hessen 17845
 
6.6%
Niedersachsen 16593
 
6.2%
Baden_Württemberg 16091
 
6.0%
Berlin 10406
 
3.9%
Thüringen 8388
 
3.1%
Rheinland_Pfalz 8368
 
3.1%
Other values (6) 28409
10.6%

Length

2024-04-03T20:29:53.707964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nordrhein_westfalen 62863
23.4%
sachsen 58154
21.6%
bayern 21609
 
8.0%
sachsen_anhalt 20124
 
7.5%
hessen 17845
 
6.6%
niedersachsen 16593
 
6.2%
baden_württemberg 16091
 
6.0%
berlin 10406
 
3.9%
thüringen 8388
 
3.1%
rheinland_pfalz 8368
 
3.1%
Other values (6) 28409
10.6%

Most occurring characters

ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3270640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3270640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3270640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

serviceCharge
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct12266
Distinct (%)4.7%
Missing6909
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean151.20611
Minimum0
Maximum146118
Zeros3434
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:29:53.996473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q195
median135
Q3190
95-th percentile300
Maximum146118
Range146118
Interquartile range (IQR)95

Descriptive statistics

Standard deviation308.29579
Coefficient of variation (CV)2.0389109
Kurtosis192110.29
Mean151.20611
Median Absolute Deviation (MAD)45
Skewness409.16432
Sum39607080
Variance95046.294
MonotonicityNot monotonic
2024-04-03T20:29:54.262164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 14293
 
5.3%
100 12995
 
4.8%
200 11147
 
4.1%
120 10650
 
4.0%
130 7455
 
2.8%
140 6997
 
2.6%
80 6979
 
2.6%
180 6812
 
2.5%
250 6422
 
2.4%
90 6337
 
2.4%
Other values (12256) 171854
63.9%
(Missing) 6909
 
2.6%
ValueCountFrequency (%)
0 3434
1.3%
0.01 5
 
< 0.1%
1 22
 
< 0.1%
1.5 3
 
< 0.1%
1.55 2
 
< 0.1%
1.7 1
 
< 0.1%
1.9 1
 
< 0.1%
2 4
 
< 0.1%
2.02 1
 
< 0.1%
2.3 3
 
< 0.1%
ValueCountFrequency (%)
146118 1
< 0.1%
25000 1
< 0.1%
20392 1
< 0.1%
15750 1
< 0.1%
10038 1
< 0.1%
9999 1
< 0.1%
6500 1
< 0.1%
6045 1
< 0.1%
3800 1
< 0.1%
3500 2
< 0.1%

heatingType
Categorical

MISSING 

Distinct13
Distinct (%)< 0.1%
Missing44856
Missing (%)16.7%
Memory size2.1 MiB
central_heating
128977 
district_heating
24808 
gas_heating
19955 
self_contained_central_heating
19087 
floor_heating
17697 
Other values (8)
13470 

Length

Max length30
Median length15
Mean length15.881412
Min length9

Characters and Unicode

Total characters3557341
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcentral_heating
2nd rowself_contained_central_heating
3rd rowfloor_heating
4th rowdistrict_heating
5th rowself_contained_central_heating

Common Values

ValueCountFrequency (%)
central_heating 128977
48.0%
district_heating 24808
 
9.2%
gas_heating 19955
 
7.4%
self_contained_central_heating 19087
 
7.1%
floor_heating 17697
 
6.6%
oil_heating 5042
 
1.9%
heat_pump 2737
 
1.0%
combined_heat_and_power_plant 1978
 
0.7%
night_storage_heater 1341
 
0.5%
wood_pellet_heating 961
 
0.4%
Other values (3) 1411
 
0.5%
(Missing) 44856
 
16.7%

Length

2024-04-03T20:29:54.465515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
central_heating 128977
57.6%
district_heating 24808
 
11.1%
gas_heating 19955
 
8.9%
self_contained_central_heating 19087
 
8.5%
floor_heating 17697
 
7.9%
oil_heating 5042
 
2.3%
heat_pump 2737
 
1.2%
combined_heat_and_power_plant 1978
 
0.9%
night_storage_heater 1341
 
0.6%
wood_pellet_heating 961
 
0.4%
Other values (3) 1411
 
0.6%

Most occurring characters

ValueCountFrequency (%)
t 447627
12.6%
e 420938
11.8%
a 416563
11.7%
n 411451
11.6%
i 295903
8.3%
_ 270404
7.6%
g 240575
6.8%
h 225335
6.3%
r 196296
5.5%
c 195739
5.5%
Other values (11) 436510
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3557341
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 447627
12.6%
e 420938
11.8%
a 416563
11.7%
n 411451
11.6%
i 295903
8.3%
_ 270404
7.6%
g 240575
6.8%
h 225335
6.3%
r 196296
5.5%
c 195739
5.5%
Other values (11) 436510
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3557341
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 447627
12.6%
e 420938
11.8%
a 416563
11.7%
n 411451
11.6%
i 295903
8.3%
_ 270404
7.6%
g 240575
6.8%
h 225335
6.3%
r 196296
5.5%
c 195739
5.5%
Other values (11) 436510
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3557341
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 447627
12.6%
e 420938
11.8%
a 416563
11.7%
n 411451
11.6%
i 295903
8.3%
_ 270404
7.6%
g 240575
6.8%
h 225335
6.3%
r 196296
5.5%
c 195739
5.5%
Other values (11) 436510
12.3%

telekomTvOffer
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing32619
Missing (%)12.1%
Memory size2.1 MiB
ONE_YEAR_FREE
227632 
NONE
 
4957
ON_DEMAND
 
3642

Length

Max length13
Median length13
Mean length12.749478
Min length4

Characters and Unicode

Total characters3011822
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowONE_YEAR_FREE
2nd rowONE_YEAR_FREE
3rd rowONE_YEAR_FREE
4th rowONE_YEAR_FREE
5th rowNONE

Common Values

ValueCountFrequency (%)
ONE_YEAR_FREE 227632
84.7%
NONE 4957
 
1.8%
ON_DEMAND 3642
 
1.4%
(Missing) 32619
 
12.1%

Length

2024-04-03T20:29:54.647652image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:29:54.848068image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
one_year_free 227632
96.4%
none 4957
 
2.1%
on_demand 3642
 
1.5%

Most occurring characters

ValueCountFrequency (%)
E 919127
30.5%
_ 458906
15.2%
R 455264
15.1%
N 244830
 
8.1%
O 236231
 
7.8%
A 231274
 
7.7%
Y 227632
 
7.6%
F 227632
 
7.6%
D 7284
 
0.2%
M 3642
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3011822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 919127
30.5%
_ 458906
15.2%
R 455264
15.1%
N 244830
 
8.1%
O 236231
 
7.8%
A 231274
 
7.7%
Y 227632
 
7.6%
F 227632
 
7.6%
D 7284
 
0.2%
M 3642
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3011822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 919127
30.5%
_ 458906
15.2%
R 455264
15.1%
N 244830
 
8.1%
O 236231
 
7.8%
A 231274
 
7.7%
Y 227632
 
7.6%
F 227632
 
7.6%
D 7284
 
0.2%
M 3642
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3011822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 919127
30.5%
_ 458906
15.2%
R 455264
15.1%
N 244830
 
8.1%
O 236231
 
7.8%
A 231274
 
7.7%
Y 227632
 
7.6%
F 227632
 
7.6%
D 7284
 
0.2%
M 3642
 
0.1%

telekomHybridUploadSpeed
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)< 0.1%
Missing223830
Missing (%)83.3%
Memory size2.1 MiB
10.0
45020 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters180080
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10.0
2nd row10.0
3rd row10.0
4th row10.0
5th row10.0

Common Values

ValueCountFrequency (%)
10.0 45020
 
16.7%
(Missing) 223830
83.3%

Length

2024-04-03T20:29:55.020875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:29:55.171906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
10.0 45020
100.0%

Most occurring characters

ValueCountFrequency (%)
0 90040
50.0%
1 45020
25.0%
. 45020
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 180080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 90040
50.0%
1 45020
25.0%
. 45020
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 180080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 90040
50.0%
1 45020
25.0%
. 45020
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 180080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 90040
50.0%
1 45020
25.0%
. 45020
25.0%

newlyConst
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
False
247679 
True
 
21171
ValueCountFrequency (%)
False 247679
92.1%
True 21171
 
7.9%
2024-04-03T20:29:55.325248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

balcony
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
True
165734 
False
103116 
ValueCountFrequency (%)
True 165734
61.6%
False 103116
38.4%
2024-04-03T20:29:55.443461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

picturecount
Real number (ℝ)

ZEROS 

Distinct95
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.7919583
Minimum0
Maximum121
Zeros4971
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:29:55.629195image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median9
Q313
95-th percentile21
Maximum121
Range121
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.4083992
Coefficient of variation (CV)0.65445531
Kurtosis9.2613945
Mean9.7919583
Median Absolute Deviation (MAD)3
Skewness1.8086855
Sum2632568
Variance41.06758
MonotonicityNot monotonic
2024-04-03T20:29:55.872957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 21930
 
8.2%
7 21429
 
8.0%
9 21421
 
8.0%
10 19819
 
7.4%
6 18848
 
7.0%
11 16741
 
6.2%
5 15605
 
5.8%
12 14430
 
5.4%
4 12315
 
4.6%
13 11909
 
4.4%
Other values (85) 94403
35.1%
ValueCountFrequency (%)
0 4971
 
1.8%
1 8965
3.3%
2 11210
4.2%
3 11536
4.3%
4 12315
4.6%
5 15605
5.8%
6 18848
7.0%
7 21429
8.0%
8 21930
8.2%
9 21421
8.0%
ValueCountFrequency (%)
121 2
 
< 0.1%
112 1
 
< 0.1%
109 1
 
< 0.1%
107 1
 
< 0.1%
101 1
 
< 0.1%
100 1
 
< 0.1%
99 5
< 0.1%
94 1
 
< 0.1%
89 2
 
< 0.1%
88 5
< 0.1%

pricetrend
Real number (ℝ)

ZEROS 

Distinct1234
Distinct (%)0.5%
Missing1832
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.3890007
Minimum-12.33
Maximum14.92
Zeros11082
Zeros (%)4.1%
Negative3754
Negative (%)1.4%
Memory size2.1 MiB
2024-04-03T20:29:56.084507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-12.33
5-th percentile0
Q12
median3.39
Q34.57
95-th percentile6.67
Maximum14.92
Range27.25
Interquartile range (IQR)2.57

Descriptive statistics

Standard deviation1.9648743
Coefficient of variation (CV)0.57977984
Kurtosis0.78832402
Mean3.3890007
Median Absolute Deviation (MAD)1.29
Skewness0.38355596
Sum904924.18
Variance3.8607309
MonotonicityNot monotonic
2024-04-03T20:29:56.311862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11082
 
4.1%
3.33 2211
 
0.8%
3.23 2042
 
0.8%
3.85 1884
 
0.7%
3.57 1720
 
0.6%
0.19 1693
 
0.6%
1.92 1656
 
0.6%
3.17 1648
 
0.6%
1.69 1640
 
0.6%
3.13 1617
 
0.6%
Other values (1224) 239825
89.2%
(Missing) 1832
 
0.7%
ValueCountFrequency (%)
-12.33 1
 
< 0.1%
-9.17 1
 
< 0.1%
-8.29 2
 
< 0.1%
-8.26 5
< 0.1%
-7.87 1
 
< 0.1%
-7.08 1
 
< 0.1%
-6.6 1
 
< 0.1%
-6.51 1
 
< 0.1%
-6.46 2
 
< 0.1%
-6.13 1
 
< 0.1%
ValueCountFrequency (%)
14.92 1
 
< 0.1%
12.87 1
 
< 0.1%
12.26 20
 
< 0.1%
12.22 8
 
< 0.1%
12.08 42
< 0.1%
12.02 33
< 0.1%
11.54 56
< 0.1%
11.34 2
 
< 0.1%
11.27 1
 
< 0.1%
11.22 61
< 0.1%

telekomUploadSpeed
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)< 0.1%
Missing33358
Missing (%)12.4%
Infinite0
Infinite (%)0.0%
Mean28.804928
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:29:56.487593image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q110
median40
Q340
95-th percentile40
Maximum100
Range99
Interquartile range (IQR)30

Descriptive statistics

Standard deviation16.337151
Coefficient of variation (CV)0.56716514
Kurtosis-1.1200502
Mean28.804928
Median Absolute Deviation (MAD)0
Skewness-0.7344649
Sum6783330.2
Variance266.90251
MonotonicityNot monotonic
2024-04-03T20:29:56.657050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
40 158296
58.9%
2.4 42858
 
15.9%
10 32889
 
12.2%
5 1036
 
0.4%
1 209
 
0.1%
100 141
 
0.1%
4 63
 
< 0.1%
(Missing) 33358
 
12.4%
ValueCountFrequency (%)
1 209
 
0.1%
2.4 42858
 
15.9%
4 63
 
< 0.1%
5 1036
 
0.4%
10 32889
 
12.2%
40 158296
58.9%
100 141
 
0.1%
ValueCountFrequency (%)
100 141
 
0.1%
40 158296
58.9%
10 32889
 
12.2%
5 1036
 
0.4%
4 63
 
< 0.1%
2.4 42858
 
15.9%
1 209
 
0.1%

totalRent
Real number (ℝ)

MISSING  SKEWED 

Distinct28486
Distinct (%)12.5%
Missing40517
Missing (%)15.1%
Infinite0
Infinite (%)0.0%
Mean901.33152
Minimum0
Maximum15751535
Zeros236
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:29:56.880037image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile325
Q1469.8
median650
Q3985
95-th percentile1795.12
Maximum15751535
Range15751535
Interquartile range (IQR)515.2

Descriptive statistics

Standard deviation33238.334
Coefficient of variation (CV)36.876924
Kurtosis220856.4
Mean901.33152
Median Absolute Deviation (MAD)220
Skewness466.56754
Sum2.0580373 × 108
Variance1.1047868 × 109
MonotonicityNot monotonic
2024-04-03T20:29:57.134325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 1897
 
0.7%
450 1892
 
0.7%
600 1805
 
0.7%
550 1668
 
0.6%
400 1500
 
0.6%
750 1495
 
0.6%
650 1486
 
0.6%
800 1427
 
0.5%
700 1417
 
0.5%
900 1399
 
0.5%
Other values (28476) 212347
79.0%
(Missing) 40517
 
15.1%
ValueCountFrequency (%)
0 236
0.1%
1 8
 
< 0.1%
1.01 1
 
< 0.1%
1.04 1
 
< 0.1%
1.08 2
 
< 0.1%
1.3 2
 
< 0.1%
2 4
 
< 0.1%
3 2
 
< 0.1%
4 5
 
< 0.1%
5.9 1
 
< 0.1%
ValueCountFrequency (%)
15751535 1
< 0.1%
1234567 1
< 0.1%
1150900 1
< 0.1%
1000000 1
< 0.1%
485350 1
< 0.1%
108000 1
< 0.1%
64651 1
< 0.1%
63204 1
< 0.1%
51570 1
< 0.1%
37600 1
< 0.1%

yearConstructed
Real number (ℝ)

MISSING 

Distinct465
Distinct (%)0.2%
Missing57045
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean1966.4006
Minimum1000
Maximum2090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:29:57.408460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1900
Q11950
median1973
Q31996
95-th percentile2019
Maximum2090
Range1090
Interquartile range (IQR)46

Descriptive statistics

Standard deviation46.992207
Coefficient of variation (CV)0.023897576
Kurtosis64.743586
Mean1966.4006
Median Absolute Deviation (MAD)23
Skewness-4.5647801
Sum4.1649348 × 108
Variance2208.2675
MonotonicityNot monotonic
2024-04-03T20:29:57.638109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 10959
 
4.1%
1900 10356
 
3.9%
2018 8759
 
3.3%
1995 4387
 
1.6%
1996 4339
 
1.6%
1910 4290
 
1.6%
2017 3903
 
1.5%
1972 3844
 
1.4%
1960 3838
 
1.4%
1970 3556
 
1.3%
Other values (455) 153574
57.1%
(Missing) 57045
 
21.2%
ValueCountFrequency (%)
1000 1
 
< 0.1%
1005 1
 
< 0.1%
1007 1
 
< 0.1%
1027 1
 
< 0.1%
1036 1
 
< 0.1%
1063 1
 
< 0.1%
1070 1
 
< 0.1%
1078 1
 
< 0.1%
1097 1
 
< 0.1%
1111 87
< 0.1%
ValueCountFrequency (%)
2090 1
 
< 0.1%
2029 1
 
< 0.1%
2026 3
 
< 0.1%
2022 2
 
< 0.1%
2021 15
 
< 0.1%
2020 2140
 
0.8%
2019 10959
4.1%
2018 8759
3.3%
2017 3903
 
1.5%
2016 2902
 
1.1%

scoutId
Real number (ℝ)

UNIQUE 

Distinct268850
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0696967 × 108
Minimum28871743
Maximum1.1571174 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:29:57.889434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum28871743
5-th percentile78243993
Q11.0669103 × 108
median1.1115838 × 108
Q31.1376876 × 108
95-th percentile1.1560063 × 108
Maximum1.1571174 × 108
Range86840000
Interquartile range (IQR)7077725.5

Descriptive statistics

Standard deviation12500933
Coefficient of variation (CV)0.11686428
Kurtosis8.3919963
Mean1.0696967 × 108
Median Absolute Deviation (MAD)3600390
Skewness-2.7733701
Sum2.8758796 × 1013
Variance1.5627334 × 1014
MonotonicityNot monotonic
2024-04-03T20:29:58.114033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96107057 1
 
< 0.1%
114446661 1
 
< 0.1%
115556774 1
 
< 0.1%
106807219 1
 
< 0.1%
112849859 1
 
< 0.1%
85750719 1
 
< 0.1%
105304397 1
 
< 0.1%
115542340 1
 
< 0.1%
113862383 1
 
< 0.1%
114473643 1
 
< 0.1%
Other values (268840) 268840
> 99.9%
ValueCountFrequency (%)
28871743 1
< 0.1%
29301391 1
< 0.1%
29301750 1
< 0.1%
29370795 1
< 0.1%
29404468 1
< 0.1%
29448107 1
< 0.1%
29506747 1
< 0.1%
29707618 1
< 0.1%
29718404 1
< 0.1%
29718866 1
< 0.1%
ValueCountFrequency (%)
115711743 1
< 0.1%
115711737 1
< 0.1%
115711660 1
< 0.1%
115711556 1
< 0.1%
115711546 1
< 0.1%
115711544 1
< 0.1%
115711543 1
< 0.1%
115711540 1
< 0.1%
115711539 1
< 0.1%
115711538 1
< 0.1%

noParkSpaces
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct71
Distinct (%)0.1%
Missing175798
Missing (%)65.4%
Infinite0
Infinite (%)0.0%
Mean1.327634
Minimum0
Maximum2241
Zeros3855
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:29:58.409152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum2241
Range2241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8.3614032
Coefficient of variation (CV)6.297973
Kurtosis55434.369
Mean1.327634
Median Absolute Deviation (MAD)0
Skewness210.59684
Sum123539
Variance69.913063
MonotonicityNot monotonic
2024-04-03T20:29:58.770519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 78814
29.3%
2 8955
 
3.3%
0 3855
 
1.4%
3 417
 
0.2%
4 137
 
0.1%
20 87
 
< 0.1%
5 74
 
< 0.1%
10 73
 
< 0.1%
6 54
 
< 0.1%
8 50
 
< 0.1%
Other values (61) 536
 
0.2%
(Missing) 175798
65.4%
ValueCountFrequency (%)
0 3855
 
1.4%
1 78814
29.3%
2 8955
 
3.3%
3 417
 
0.2%
4 137
 
0.1%
5 74
 
< 0.1%
6 54
 
< 0.1%
7 31
 
< 0.1%
8 50
 
< 0.1%
9 32
 
< 0.1%
ValueCountFrequency (%)
2241 1
 
< 0.1%
320 3
 
< 0.1%
310 1
 
< 0.1%
150 14
< 0.1%
130 2
 
< 0.1%
126 2
 
< 0.1%
99 3
 
< 0.1%
95 7
< 0.1%
93 1
 
< 0.1%
90 4
 
< 0.1%

firingTypes
Text

MISSING 

Distinct132
Distinct (%)0.1%
Missing56964
Missing (%)21.2%
Memory size2.1 MiB
2024-04-03T20:29:59.042870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length187
Median length3
Mean length8.131113
Min length3

Characters and Unicode

Total characters1722869
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)< 0.1%

Sample

1st rowoil
2nd rowgas
3rd rowdistrict_heating
4th rowgas
5th rowgas
ValueCountFrequency (%)
gas 110899
52.3%
district_heating 49389
23.3%
oil 18137
 
8.6%
natural_gas_light 10080
 
4.8%
electricity 4838
 
2.3%
natural_gas_heavy 4542
 
2.1%
pellet_heating 2478
 
1.2%
geothermal 2442
 
1.2%
gas:electricity 1354
 
0.6%
local_heating 932
 
0.4%
Other values (122) 6795
 
3.2%
2024-04-03T20:29:59.519999image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 228894
13.3%
t 206300
12.0%
i 204968
11.9%
g 198497
11.5%
s 184703
10.7%
e 96892
 
5.6%
_ 95360
 
5.5%
r 81295
 
4.7%
n 77540
 
4.5%
h 75928
 
4.4%
Other values (15) 272492
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1722869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 228894
13.3%
t 206300
12.0%
i 204968
11.9%
g 198497
11.5%
s 184703
10.7%
e 96892
 
5.6%
_ 95360
 
5.5%
r 81295
 
4.7%
n 77540
 
4.5%
h 75928
 
4.4%
Other values (15) 272492
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1722869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 228894
13.3%
t 206300
12.0%
i 204968
11.9%
g 198497
11.5%
s 184703
10.7%
e 96892
 
5.6%
_ 95360
 
5.5%
r 81295
 
4.7%
n 77540
 
4.5%
h 75928
 
4.4%
Other values (15) 272492
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1722869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 228894
13.3%
t 206300
12.0%
i 204968
11.9%
g 198497
11.5%
s 184703
10.7%
e 96892
 
5.6%
_ 95360
 
5.5%
r 81295
 
4.7%
n 77540
 
4.5%
h 75928
 
4.4%
Other values (15) 272492
15.8%

hasKitchen
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
False
176794 
True
92056 
ValueCountFrequency (%)
False 176794
65.8%
True 92056
34.2%
2024-04-03T20:29:59.694381image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

geo_bln
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Nordrhein_Westfalen
62863 
Sachsen
58154 
Bayern
21609 
Sachsen_Anhalt
20124 
Hessen
17845 
Other values (11)
88255 

Length

Max length22
Median length18
Mean length12.165297
Min length6

Characters and Unicode

Total characters3270640
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNordrhein_Westfalen
2nd rowRheinland_Pfalz
3rd rowSachsen
4th rowSachsen
5th rowBremen

Common Values

ValueCountFrequency (%)
Nordrhein_Westfalen 62863
23.4%
Sachsen 58154
21.6%
Bayern 21609
 
8.0%
Sachsen_Anhalt 20124
 
7.5%
Hessen 17845
 
6.6%
Niedersachsen 16593
 
6.2%
Baden_Württemberg 16091
 
6.0%
Berlin 10406
 
3.9%
Thüringen 8388
 
3.1%
Rheinland_Pfalz 8368
 
3.1%
Other values (6) 28409
10.6%

Length

2024-04-03T20:29:59.915402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nordrhein_westfalen 62863
23.4%
sachsen 58154
21.6%
bayern 21609
 
8.0%
sachsen_anhalt 20124
 
7.5%
hessen 17845
 
6.6%
niedersachsen 16593
 
6.2%
baden_württemberg 16091
 
6.0%
berlin 10406
 
3.9%
thüringen 8388
 
3.1%
rheinland_pfalz 8368
 
3.1%
Other values (6) 28409
10.6%

Most occurring characters

ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3270640
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3270640
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3270640
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 495502
15.2%
n 378422
 
11.6%
r 256867
 
7.9%
a 247294
 
7.6%
s 223353
 
6.8%
h 201282
 
6.2%
l 131528
 
4.0%
t 121837
 
3.7%
_ 120748
 
3.7%
i 119954
 
3.7%
Other values (25) 973853
29.8%

cellar
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
True
172235 
False
96615 
ValueCountFrequency (%)
True 172235
64.1%
False 96615
35.9%
2024-04-03T20:30:00.112867image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

yearConstructedRange
Real number (ℝ)

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing57045
Missing (%)21.2%
Infinite0
Infinite (%)0.0%
Mean3.714544
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:00.253578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q35
95-th percentile9
Maximum9
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7381335
Coefficient of variation (CV)0.73713853
Kurtosis-0.57569938
Mean3.714544
Median Absolute Deviation (MAD)2
Skewness0.85823642
Sum786759
Variance7.4973752
MonotonicityNot monotonic
2024-04-03T20:30:00.442793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 54203
20.2%
2 45661
17.0%
9 28685
10.7%
5 26291
9.8%
3 24262
9.0%
4 18011
 
6.7%
8 7578
 
2.8%
6 3638
 
1.4%
7 3476
 
1.3%
(Missing) 57045
21.2%
ValueCountFrequency (%)
1 54203
20.2%
2 45661
17.0%
3 24262
9.0%
4 18011
 
6.7%
5 26291
9.8%
6 3638
 
1.4%
7 3476
 
1.3%
8 7578
 
2.8%
9 28685
10.7%
ValueCountFrequency (%)
9 28685
10.7%
8 7578
 
2.8%
7 3476
 
1.3%
6 3638
 
1.4%
5 26291
9.8%
4 18011
 
6.7%
3 24262
9.0%
2 45661
17.0%
1 54203
20.2%

baseRent
Real number (ℝ)

SKEWED 

Distinct26659
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean694.12943
Minimum0
Maximum9999999
Zeros89
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:00.696185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile230
Q1338
median490
Q3799
95-th percentile1500
Maximum9999999
Range9999999
Interquartile range (IQR)461

Descriptive statistics

Standard deviation19536.018
Coefficient of variation (CV)28.144632
Kurtosis255373.17
Mean694.12943
Median Absolute Deviation (MAD)190
Skewness500.20195
Sum1.866167 × 108
Variance3.8165598 × 108
MonotonicityNot monotonic
2024-04-03T20:30:00.951174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350 3810
 
1.4%
450 3558
 
1.3%
300 3152
 
1.2%
400 2985
 
1.1%
650 2905
 
1.1%
550 2749
 
1.0%
750 2718
 
1.0%
500 2535
 
0.9%
600 2511
 
0.9%
320 2480
 
0.9%
Other values (26649) 239447
89.1%
ValueCountFrequency (%)
0 89
< 0.1%
1 8
 
< 0.1%
3 3
 
< 0.1%
3.01 1
 
< 0.1%
3.9 2
 
< 0.1%
4.81 1
 
< 0.1%
4.96 1
 
< 0.1%
5.25 1
 
< 0.1%
5.9 1
 
< 0.1%
6 2
 
< 0.1%
ValueCountFrequency (%)
9999999 1
< 0.1%
1234567 1
< 0.1%
1000000 1
< 0.1%
120000 2
< 0.1%
39200 1
< 0.1%
30990 1
< 0.1%
20100 1
< 0.1%
20000 1
< 0.1%
19329 1
< 0.1%
17781.12 1
< 0.1%

houseNumber
Text

MISSING 

Distinct5510
Distinct (%)2.8%
Missing71018
Missing (%)26.4%
Memory size2.1 MiB
2024-04-03T20:30:01.455412image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length2
Mean length1.9932771
Min length1

Characters and Unicode

Total characters394334
Distinct characters84
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2887 ?
Unique (%)1.5%

Sample

1st row244
2nd row4
3rd row35
4th row10
5th row1e
ValueCountFrequency (%)
1 7453
 
3.7%
2 7049
 
3.5%
4 6037
 
3.0%
3 5892
 
2.9%
5 5660
 
2.8%
6 5326
 
2.6%
8 5054
 
2.5%
7 4995
 
2.5%
10 4638
 
2.3%
9 4438
 
2.2%
Other values (3446) 147058
72.2%
2024-04-03T20:30:02.153520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 80129
20.3%
2 55179
14.0%
3 41023
10.4%
4 35162
8.9%
5 30470
 
7.7%
6 26831
 
6.8%
7 23967
 
6.1%
0 23618
 
6.0%
8 22523
 
5.7%
9 19886
 
5.0%
Other values (74) 35546
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 394334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 80129
20.3%
2 55179
14.0%
3 41023
10.4%
4 35162
8.9%
5 30470
 
7.7%
6 26831
 
6.8%
7 23967
 
6.1%
0 23618
 
6.0%
8 22523
 
5.7%
9 19886
 
5.0%
Other values (74) 35546
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 394334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 80129
20.3%
2 55179
14.0%
3 41023
10.4%
4 35162
8.9%
5 30470
 
7.7%
6 26831
 
6.8%
7 23967
 
6.1%
0 23618
 
6.0%
8 22523
 
5.7%
9 19886
 
5.0%
Other values (74) 35546
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 394334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 80129
20.3%
2 55179
14.0%
3 41023
10.4%
4 35162
8.9%
5 30470
 
7.7%
6 26831
 
6.8%
7 23967
 
6.1%
0 23618
 
6.0%
8 22523
 
5.7%
9 19886
 
5.0%
Other values (74) 35546
9.0%

livingSpace
Real number (ℝ)

SKEWED 

Distinct13005
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.355548
Minimum0
Maximum111111
Zeros75
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:02.417524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.61
Q154
median67.32
Q387
95-th percentile131.732
Maximum111111
Range111111
Interquartile range (IQR)33

Descriptive statistics

Standard deviation254.75921
Coefficient of variation (CV)3.42623
Kurtosis151058.26
Mean74.355548
Median Absolute Deviation (MAD)15.75
Skewness373.83926
Sum19990489
Variance64902.254
MonotonicityNot monotonic
2024-04-03T20:30:02.630352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 5189
 
1.9%
70 4154
 
1.5%
80 4031
 
1.5%
65 3808
 
1.4%
75 3692
 
1.4%
50 3423
 
1.3%
90 3104
 
1.2%
100 3006
 
1.1%
55 2979
 
1.1%
85 2639
 
1.0%
Other values (12995) 232825
86.6%
ValueCountFrequency (%)
0 75
< 0.1%
1 18
 
< 0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
5 1
 
< 0.1%
5.5 1
 
< 0.1%
5.58 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
7.7 1
 
< 0.1%
ValueCountFrequency (%)
111111 1
< 0.1%
66100 1
< 0.1%
14000 1
< 0.1%
10259 1
< 0.1%
8684 1
< 0.1%
7008 1
< 0.1%
4947 1
< 0.1%
4340 1
< 0.1%
2782 1
< 0.1%
2420 1
< 0.1%
Distinct419
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:02.843943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length39
Median length29
Mean length11.808168
Min length3

Characters and Unicode

Total characters3174626
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDortmund
2nd rowRhein_Pfalz_Kreis
3rd rowDresden
4th rowMittelsachsen_Kreis
5th rowBremen
ValueCountFrequency (%)
leipzig 13723
 
5.1%
chemnitz 12575
 
4.7%
berlin 10406
 
3.9%
dresden 7522
 
2.8%
magdeburg 4860
 
1.8%
halle_saale 4565
 
1.7%
münchen 4383
 
1.6%
essen 4351
 
1.6%
frankfurt_am_main 4296
 
1.6%
hamburg 3759
 
1.4%
Other values (409) 198410
73.8%
2024-04-03T20:30:03.296868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3174626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3174626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3174626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

condition
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing68489
Missing (%)25.5%
Memory size2.1 MiB
well_kept
66591 
refurbished
26964 
fully_renovated
26368 
first_time_use
21959 
mint_condition
21938 
Other values (5)
36541 

Length

Max length34
Median length19
Mean length13.272039
Min length9

Characters and Unicode

Total characters2659199
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwell_kept
2nd rowrefurbished
3rd rowfirst_time_use
4th rowrefurbished
5th rowwell_kept

Common Values

ValueCountFrequency (%)
well_kept 66591
24.8%
refurbished 26964
 
10.0%
fully_renovated 26368
 
9.8%
first_time_use 21959
 
8.2%
mint_condition 21938
 
8.2%
modernized 17226
 
6.4%
first_time_use_after_refurbishment 15699
 
5.8%
negotiable 2240
 
0.8%
need_of_renovation 1372
 
0.5%
ripe_for_demolition 4
 
< 0.1%
(Missing) 68489
25.5%

Length

2024-04-03T20:30:03.494685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:03.712584image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
well_kept 66591
33.2%
refurbished 26964
13.5%
fully_renovated 26368
 
13.2%
first_time_use 21959
 
11.0%
mint_condition 21938
 
10.9%
modernized 17226
 
8.6%
first_time_use_after_refurbishment 15699
 
7.8%
negotiable 2240
 
1.1%
need_of_renovation 1372
 
0.7%
ripe_for_demolition 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 405315
15.2%
t 247165
 
9.3%
_ 224363
 
8.4%
i 204643
 
7.7%
l 188162
 
7.1%
r 183657
 
6.9%
n 131467
 
4.9%
f 123764
 
4.7%
s 117979
 
4.4%
d 111098
 
4.2%
Other values (14) 721586
27.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2659199
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 405315
15.2%
t 247165
 
9.3%
_ 224363
 
8.4%
i 204643
 
7.7%
l 188162
 
7.1%
r 183657
 
6.9%
n 131467
 
4.9%
f 123764
 
4.7%
s 117979
 
4.4%
d 111098
 
4.2%
Other values (14) 721586
27.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2659199
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 405315
15.2%
t 247165
 
9.3%
_ 224363
 
8.4%
i 204643
 
7.7%
l 188162
 
7.1%
r 183657
 
6.9%
n 131467
 
4.9%
f 123764
 
4.7%
s 117979
 
4.4%
d 111098
 
4.2%
Other values (14) 721586
27.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2659199
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 405315
15.2%
t 247165
 
9.3%
_ 224363
 
8.4%
i 204643
 
7.7%
l 188162
 
7.1%
r 183657
 
6.9%
n 131467
 
4.9%
f 123764
 
4.7%
s 117979
 
4.4%
d 111098
 
4.2%
Other values (14) 721586
27.1%

interiorQual
Categorical

MISSING 

Distinct4
Distinct (%)< 0.1%
Missing112665
Missing (%)41.9%
Memory size2.1 MiB
normal
81826 
sophisticated
64762 
luxury
 
7648
simple
 
1949

Length

Max length13
Median length6
Mean length8.9025451
Min length6

Characters and Unicode

Total characters1390444
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal
2nd rownormal
3rd rowsophisticated
4th rowsophisticated
5th rownormal

Common Values

ValueCountFrequency (%)
normal 81826
30.4%
sophisticated 64762
24.1%
luxury 7648
 
2.8%
simple 1949
 
0.7%
(Missing) 112665
41.9%

Length

2024-04-03T20:30:03.990050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:04.182800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
normal 81826
52.4%
sophisticated 64762
41.5%
luxury 7648
 
4.9%
simple 1949
 
1.2%

Most occurring characters

ValueCountFrequency (%)
a 146588
10.5%
o 146588
10.5%
s 131473
9.5%
i 131473
9.5%
t 129524
9.3%
l 91423
 
6.6%
r 89474
 
6.4%
m 83775
 
6.0%
n 81826
 
5.9%
e 66711
 
4.8%
Other values (7) 291589
21.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1390444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 146588
10.5%
o 146588
10.5%
s 131473
9.5%
i 131473
9.5%
t 129524
9.3%
l 91423
 
6.6%
r 89474
 
6.4%
m 83775
 
6.0%
n 81826
 
5.9%
e 66711
 
4.8%
Other values (7) 291589
21.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1390444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 146588
10.5%
o 146588
10.5%
s 131473
9.5%
i 131473
9.5%
t 129524
9.3%
l 91423
 
6.6%
r 89474
 
6.4%
m 83775
 
6.0%
n 81826
 
5.9%
e 66711
 
4.8%
Other values (7) 291589
21.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1390444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 146588
10.5%
o 146588
10.5%
s 131473
9.5%
i 131473
9.5%
t 129524
9.3%
l 91423
 
6.6%
r 89474
 
6.4%
m 83775
 
6.0%
n 81826
 
5.9%
e 66711
 
4.8%
Other values (7) 291589
21.0%

petsAllowed
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing114573
Missing (%)42.6%
Memory size2.1 MiB
negotiable
91970 
no
51991 
yes
10316 

Length

Max length10
Median length10
Mean length6.8359509
Min length2

Characters and Unicode

Total characters1054630
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rownegotiable
5th rownegotiable

Common Values

ValueCountFrequency (%)
negotiable 91970
34.2%
no 51991
19.3%
yes 10316
 
3.8%
(Missing) 114573
42.6%

Length

2024-04-03T20:30:04.382623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:04.558481image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
negotiable 91970
59.6%
no 51991
33.7%
yes 10316
 
6.7%

Most occurring characters

ValueCountFrequency (%)
e 194256
18.4%
n 143961
13.7%
o 143961
13.7%
g 91970
8.7%
t 91970
8.7%
i 91970
8.7%
a 91970
8.7%
b 91970
8.7%
l 91970
8.7%
y 10316
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1054630
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 194256
18.4%
n 143961
13.7%
o 143961
13.7%
g 91970
8.7%
t 91970
8.7%
i 91970
8.7%
a 91970
8.7%
b 91970
8.7%
l 91970
8.7%
y 10316
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1054630
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 194256
18.4%
n 143961
13.7%
o 143961
13.7%
g 91970
8.7%
t 91970
8.7%
i 91970
8.7%
a 91970
8.7%
b 91970
8.7%
l 91970
8.7%
y 10316
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1054630
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 194256
18.4%
n 143961
13.7%
o 143961
13.7%
g 91970
8.7%
t 91970
8.7%
i 91970
8.7%
a 91970
8.7%
b 91970
8.7%
l 91970
8.7%
y 10316
 
1.0%

street
Text

Distinct52373
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:04.964683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length89
Median length61
Mean length16.631378
Min length1

Characters and Unicode

Total characters4471346
Distinct characters85
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29419 ?
Unique (%)10.9%

Sample

1st rowSch&uuml;ruferstra&szlig;e
2nd rowno_information
3rd rowTurnerweg
4th rowGl&uuml;ck-Auf-Stra&szlig;e
5th rowHermann-Henrich-Meier-Allee
ValueCountFrequency (%)
no_information 71013
 
21.1%
stra&szlig;e 21786
 
6.5%
str 13707
 
4.1%
am 6439
 
1.9%
der 2636
 
0.8%
weg 2484
 
0.7%
an 1579
 
0.5%
im 1271
 
0.4%
hauptstra&szlig;e 1093
 
0.3%
strasse 1030
 
0.3%
Other values (45483) 214152
63.5%
2024-04-03T20:30:05.605612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 406206
 
9.1%
e 374065
 
8.4%
n 348661
 
7.8%
i 333808
 
7.5%
t 303070
 
6.8%
o 280989
 
6.3%
a 279635
 
6.3%
s 262963
 
5.9%
l 210075
 
4.7%
g 150973
 
3.4%
Other values (75) 1520901
34.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4471346
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 406206
 
9.1%
e 374065
 
8.4%
n 348661
 
7.8%
i 333808
 
7.5%
t 303070
 
6.8%
o 280989
 
6.3%
a 279635
 
6.3%
s 262963
 
5.9%
l 210075
 
4.7%
g 150973
 
3.4%
Other values (75) 1520901
34.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4471346
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 406206
 
9.1%
e 374065
 
8.4%
n 348661
 
7.8%
i 333808
 
7.5%
t 303070
 
6.8%
o 280989
 
6.3%
a 279635
 
6.3%
s 262963
 
5.9%
l 210075
 
4.7%
g 150973
 
3.4%
Other values (75) 1520901
34.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4471346
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 406206
 
9.1%
e 374065
 
8.4%
n 348661
 
7.8%
i 333808
 
7.5%
t 303070
 
6.8%
o 280989
 
6.3%
a 279635
 
6.3%
s 262963
 
5.9%
l 210075
 
4.7%
g 150973
 
3.4%
Other values (75) 1520901
34.0%

streetPlain
Text

MISSING 

Distinct54490
Distinct (%)27.5%
Missing71013
Missing (%)26.4%
Memory size2.1 MiB
2024-04-03T20:30:05.963734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length67
Median length48
Mean length14.165586
Min length1

Characters and Unicode

Total characters2802477
Distinct characters96
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31068 ?
Unique (%)15.7%

Sample

1st rowSchüruferstraße
2nd rowTurnerweg
3rd rowGlück-Auf-Straße
4th rowHermann-Henrich-Meier-Allee
5th rowHardeseiche
ValueCountFrequency (%)
hauptstraße 941
 
0.5%
bahnhofstraße 855
 
0.4%
leipziger_straße 440
 
0.2%
bahnhofstr 422
 
0.2%
goethestraße 417
 
0.2%
berliner_straße 381
 
0.2%
schillerstraße 380
 
0.2%
hauptstr 362
 
0.2%
gartenstraße 360
 
0.2%
schulstraße 350
 
0.2%
Other values (51186) 192929
97.5%
2024-04-03T20:30:06.521274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 373823
 
13.3%
r 335190
 
12.0%
t 231928
 
8.3%
a 203791
 
7.3%
s 170051
 
6.1%
n 135622
 
4.8%
i 98872
 
3.5%
ß 92910
 
3.3%
l 92702
 
3.3%
S 85131
 
3.0%
Other values (86) 982457
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2802477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 373823
 
13.3%
r 335190
 
12.0%
t 231928
 
8.3%
a 203791
 
7.3%
s 170051
 
6.1%
n 135622
 
4.8%
i 98872
 
3.5%
ß 92910
 
3.3%
l 92702
 
3.3%
S 85131
 
3.0%
Other values (86) 982457
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2802477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 373823
 
13.3%
r 335190
 
12.0%
t 231928
 
8.3%
a 203791
 
7.3%
s 170051
 
6.1%
n 135622
 
4.8%
i 98872
 
3.5%
ß 92910
 
3.3%
l 92702
 
3.3%
S 85131
 
3.0%
Other values (86) 982457
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2802477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 373823
 
13.3%
r 335190
 
12.0%
t 231928
 
8.3%
a 203791
 
7.3%
s 170051
 
6.1%
n 135622
 
4.8%
i 98872
 
3.5%
ß 92910
 
3.3%
l 92702
 
3.3%
S 85131
 
3.0%
Other values (86) 982457
35.1%

lift
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
False
205528 
True
63322 
ValueCountFrequency (%)
False 205528
76.4%
True 63322
 
23.6%
2024-04-03T20:30:06.701590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

baseRentRange
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7652557
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:06.835564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2143571
Coefficient of variation (CV)0.5881027
Kurtosis-0.8951495
Mean3.7652557
Median Absolute Deviation (MAD)2
Skewness0.44722718
Sum1012289
Variance4.9033772
MonotonicityNot monotonic
2024-04-03T20:30:06.998495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2 54546
20.3%
1 48225
17.9%
5 38000
14.1%
3 37138
13.8%
7 27495
10.2%
4 25808
9.6%
6 24210
9.0%
8 8251
 
3.1%
9 5177
 
1.9%
ValueCountFrequency (%)
1 48225
17.9%
2 54546
20.3%
3 37138
13.8%
4 25808
9.6%
5 38000
14.1%
6 24210
9.0%
7 27495
10.2%
8 8251
 
3.1%
9 5177
 
1.9%
ValueCountFrequency (%)
9 5177
 
1.9%
8 8251
 
3.1%
7 27495
10.2%
6 24210
9.0%
5 38000
14.1%
4 25808
9.6%
3 37138
13.8%
2 54546
20.3%
1 48225
17.9%

typeOfFlat
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing36614
Missing (%)13.6%
Memory size2.1 MiB
apartment
131522 
roof_storey
34787 
ground_floor
31538 
other
 
9519
maisonette
 
9319
Other values (5)
15551 

Length

Max length19
Median length9
Mean length9.8978711
Min length4

Characters and Unicode

Total characters2298642
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowground_floor
2nd rowground_floor
3rd rowapartment
4th rowother
5th rowapartment

Common Values

ValueCountFrequency (%)
apartment 131522
48.9%
roof_storey 34787
 
12.9%
ground_floor 31538
 
11.7%
other 9519
 
3.5%
maisonette 9319
 
3.5%
raised_ground_floor 5628
 
2.1%
penthouse 3568
 
1.3%
terraced_flat 3385
 
1.3%
half_basement 2013
 
0.7%
loft 957
 
0.4%
(Missing) 36614
 
13.6%

Length

2024-04-03T20:30:07.207372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:07.423449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
apartment 131522
56.6%
roof_storey 34787
 
15.0%
ground_floor 31538
 
13.6%
other 9519
 
4.1%
maisonette 9319
 
4.0%
raised_ground_floor 5628
 
2.4%
penthouse 3568
 
1.5%
terraced_flat 3385
 
1.5%
half_basement 2013
 
0.9%
loft 957
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 339296
14.8%
r 297345
12.9%
a 288787
12.6%
o 239222
10.4%
e 218026
9.5%
n 183588
8.0%
m 142854
6.2%
p 135090
 
5.9%
_ 82979
 
3.6%
f 78308
 
3.4%
Other values (10) 293147
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2298642
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 339296
14.8%
r 297345
12.9%
a 288787
12.6%
o 239222
10.4%
e 218026
9.5%
n 183588
8.0%
m 142854
6.2%
p 135090
 
5.9%
_ 82979
 
3.6%
f 78308
 
3.4%
Other values (10) 293147
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2298642
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 339296
14.8%
r 297345
12.9%
a 288787
12.6%
o 239222
10.4%
e 218026
9.5%
n 183588
8.0%
m 142854
6.2%
p 135090
 
5.9%
_ 82979
 
3.6%
f 78308
 
3.4%
Other values (10) 293147
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2298642
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 339296
14.8%
r 297345
12.9%
a 288787
12.6%
o 239222
10.4%
e 218026
9.5%
n 183588
8.0%
m 142854
6.2%
p 135090
 
5.9%
_ 82979
 
3.6%
f 78308
 
3.4%
Other values (10) 293147
12.8%

geo_plz
Real number (ℝ)

Distinct7634
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37283.022
Minimum852
Maximum99998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:07.669133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum852
5-th percentile2703.05
Q19128
median38667
Q357072
95-th percentile89077
Maximum99998
Range99146
Interquartile range (IQR)47944

Descriptive statistics

Standard deviation27798.037
Coefficient of variation (CV)0.74559506
Kurtosis-0.86720703
Mean37283.022
Median Absolute Deviation (MAD)25612
Skewness0.43060489
Sum1.0023541 × 1010
Variance7.7273088 × 108
MonotonicityNot monotonic
2024-04-03T20:30:07.919539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9130 2008
 
0.7%
9126 1994
 
0.7%
9131 1649
 
0.6%
9112 1626
 
0.6%
9113 1416
 
0.5%
8056 1059
 
0.4%
4157 975
 
0.4%
39112 923
 
0.3%
6217 887
 
0.3%
9599 862
 
0.3%
Other values (7624) 255451
95.0%
ValueCountFrequency (%)
852 2
 
< 0.1%
853 1
 
< 0.1%
1057 3
 
< 0.1%
1067 795
0.3%
1069 224
 
0.1%
1097 404
0.2%
1099 464
0.2%
1108 48
 
< 0.1%
1109 113
 
< 0.1%
1127 201
 
0.1%
ValueCountFrequency (%)
99998 2
 
< 0.1%
99994 10
 
< 0.1%
99991 9
 
< 0.1%
99986 2
 
< 0.1%
99976 9
 
< 0.1%
99974 310
0.1%
99958 1
 
< 0.1%
99955 3
 
< 0.1%
99947 108
 
< 0.1%
99898 1
 
< 0.1%

noRooms
Real number (ℝ)

SKEWED 

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6412611
Minimum1
Maximum999.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:08.153367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum999.99
Range998.99
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6334396
Coefficient of variation (CV)0.99703872
Kurtosis85074.669
Mean2.6412611
Median Absolute Deviation (MAD)1
Skewness249.53628
Sum710103.05
Variance6.9350041
MonotonicityNot monotonic
2024-04-03T20:30:08.369994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 92089
34.3%
2 89038
33.1%
1 27805
 
10.3%
4 27776
 
10.3%
2.5 9583
 
3.6%
3.5 8897
 
3.3%
5 5726
 
2.1%
1.5 3405
 
1.3%
4.5 2074
 
0.8%
6 1308
 
0.5%
Other values (54) 1149
 
0.4%
ValueCountFrequency (%)
1 27805
 
10.3%
1.1 5
 
< 0.1%
1.2 1
 
< 0.1%
1.25 1
 
< 0.1%
1.3 1
 
< 0.1%
1.5 3405
 
1.3%
2 89038
33.1%
2.01 4
 
< 0.1%
2.1 67
 
< 0.1%
2.2 32
 
< 0.1%
ValueCountFrequency (%)
999.99 1
< 0.1%
560 1
< 0.1%
305 1
< 0.1%
230 1
< 0.1%
200 1
< 0.1%
160 1
< 0.1%
140 1
< 0.1%
120 1
< 0.1%
100 1
< 0.1%
99.5 1
< 0.1%

thermalChar
Real number (ℝ)

MISSING 

Distinct7847
Distinct (%)4.8%
Missing106506
Missing (%)39.6%
Infinite0
Infinite (%)0.0%
Mean114.74953
Minimum0.1
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:08.577048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile42
Q179
median107
Q3140.3
95-th percentile208.8
Maximum1996
Range1995.9
Interquartile range (IQR)61.3

Descriptive statistics

Standard deviation61.653663
Coefficient of variation (CV)0.53728901
Kurtosis142.81989
Mean114.74953
Median Absolute Deviation (MAD)30
Skewness6.7931659
Sum18628898
Variance3801.1741
MonotonicityNot monotonic
2024-04-03T20:30:08.816845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 932
 
0.3%
100 913
 
0.3%
114 876
 
0.3%
80 871
 
0.3%
87 863
 
0.3%
79 859
 
0.3%
90 850
 
0.3%
85 846
 
0.3%
96 841
 
0.3%
98 840
 
0.3%
Other values (7837) 153653
57.2%
(Missing) 106506
39.6%
ValueCountFrequency (%)
0.1 1
 
< 0.1%
0.2 8
< 0.1%
0.25 2
 
< 0.1%
0.27 1
 
< 0.1%
0.28 1
 
< 0.1%
0.3 11
< 0.1%
0.39 1
 
< 0.1%
0.46 1
 
< 0.1%
0.49 1
 
< 0.1%
0.64 1
 
< 0.1%
ValueCountFrequency (%)
1996 1
< 0.1%
1983 1
< 0.1%
1974 1
< 0.1%
1971 1
< 0.1%
1932 1
< 0.1%
1900 2
< 0.1%
1897 2
< 0.1%
1890 1
< 0.1%
1752 1
< 0.1%
1745 1
< 0.1%

floor
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct53
Distinct (%)< 0.1%
Missing51309
Missing (%)19.1%
Infinite0
Infinite (%)0.0%
Mean2.1224045
Minimum-1
Maximum999
Zeros24604
Zeros (%)9.2%
Negative314
Negative (%)0.1%
Memory size2.1 MiB
2024-04-03T20:30:09.075928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum999
Range1000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.634934
Coefficient of variation (CV)1.712649
Kurtosis35848.49
Mean2.1224045
Median Absolute Deviation (MAD)1
Skewness155.43806
Sum461710
Variance13.212745
MonotonicityNot monotonic
2024-04-03T20:30:09.307065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 64133
23.9%
2 56937
21.2%
3 37879
14.1%
0 24604
 
9.2%
4 19953
 
7.4%
5 7987
 
3.0%
6 2493
 
0.9%
7 1031
 
0.4%
8 578
 
0.2%
9 427
 
0.2%
Other values (43) 1519
 
0.6%
(Missing) 51309
19.1%
ValueCountFrequency (%)
-1 314
 
0.1%
0 24604
 
9.2%
1 64133
23.9%
2 56937
21.2%
3 37879
14.1%
4 19953
 
7.4%
5 7987
 
3.0%
6 2493
 
0.9%
7 1031
 
0.4%
8 578
 
0.2%
ValueCountFrequency (%)
999 1
 
< 0.1%
650 1
 
< 0.1%
645 1
 
< 0.1%
390 1
 
< 0.1%
139 1
 
< 0.1%
138 1
 
< 0.1%
137 2
< 0.1%
136 3
< 0.1%
135 1
 
< 0.1%
134 1
 
< 0.1%

numberOfFloors
Real number (ℝ)

MISSING  SKEWED 

Distinct57
Distinct (%)< 0.1%
Missing97732
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean3.5723185
Minimum0
Maximum999
Zeros1371
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:09.511575image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum999
Range999
Interquartile range (IQR)2

Descriptive statistics

Standard deviation6.3754962
Coefficient of variation (CV)1.7846942
Kurtosis15489.764
Mean3.5723185
Median Absolute Deviation (MAD)1
Skewness114.09243
Sum611288
Variance40.646952
MonotonicityNot monotonic
2024-04-03T20:30:09.737920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 50822
18.9%
4 39862
14.8%
2 37350
 
13.9%
5 19833
 
7.4%
1 8463
 
3.1%
6 6282
 
2.3%
7 2555
 
1.0%
0 1371
 
0.5%
8 1133
 
0.4%
11 1004
 
0.4%
Other values (47) 2443
 
0.9%
(Missing) 97732
36.4%
ValueCountFrequency (%)
0 1371
 
0.5%
1 8463
 
3.1%
2 37350
13.9%
3 50822
18.9%
4 39862
14.8%
5 19833
 
7.4%
6 6282
 
2.3%
7 2555
 
1.0%
8 1133
 
0.4%
9 514
 
0.2%
ValueCountFrequency (%)
999 3
< 0.1%
800 2
< 0.1%
730 1
 
< 0.1%
600 1
 
< 0.1%
594 1
 
< 0.1%
410 1
 
< 0.1%
400 1
 
< 0.1%
378 1
 
< 0.1%
370 1
 
< 0.1%
301 1
 
< 0.1%

noRoomsRange
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
3
100992 
2
98728 
1
31218 
4
29851 
5
 
8061

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters268850
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 100992
37.6%
2 98728
36.7%
1 31218
 
11.6%
4 29851
 
11.1%
5 8061
 
3.0%

Length

2024-04-03T20:30:09.929768image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:10.092338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 100992
37.6%
2 98728
36.7%
1 31218
 
11.6%
4 29851
 
11.1%
5 8061
 
3.0%

Most occurring characters

ValueCountFrequency (%)
3 100992
37.6%
2 98728
36.7%
1 31218
 
11.6%
4 29851
 
11.1%
5 8061
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268850
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 100992
37.6%
2 98728
36.7%
1 31218
 
11.6%
4 29851
 
11.1%
5 8061
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268850
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 100992
37.6%
2 98728
36.7%
1 31218
 
11.6%
4 29851
 
11.1%
5 8061
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268850
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 100992
37.6%
2 98728
36.7%
1 31218
 
11.6%
4 29851
 
11.1%
5 8061
 
3.0%

garden
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size262.7 KiB
False
216093 
True
52757 
ValueCountFrequency (%)
False 216093
80.4%
True 52757
 
19.6%
2024-04-03T20:30:10.240995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

livingSpaceRange
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0707904
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:10.364189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.407127
Coefficient of variation (CV)0.45822957
Kurtosis0.2979037
Mean3.0707904
Median Absolute Deviation (MAD)1
Skewness0.77686978
Sum825582
Variance1.9800063
MonotonicityNot monotonic
2024-04-03T20:30:10.504221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 82458
30.7%
2 76383
28.4%
4 42789
15.9%
1 27057
 
10.1%
5 20523
 
7.6%
6 12866
 
4.8%
7 6774
 
2.5%
ValueCountFrequency (%)
1 27057
 
10.1%
2 76383
28.4%
3 82458
30.7%
4 42789
15.9%
5 20523
 
7.6%
6 12866
 
4.8%
7 6774
 
2.5%
ValueCountFrequency (%)
7 6774
 
2.5%
6 12866
 
4.8%
5 20523
 
7.6%
4 42789
15.9%
3 82458
30.7%
2 76383
28.4%
1 27057
 
10.1%

regio2
Text

Distinct419
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:10.717942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length39
Median length29
Mean length11.808168
Min length3

Characters and Unicode

Total characters3174626
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDortmund
2nd rowRhein_Pfalz_Kreis
3rd rowDresden
4th rowMittelsachsen_Kreis
5th rowBremen
ValueCountFrequency (%)
leipzig 13723
 
5.1%
chemnitz 12575
 
4.7%
berlin 10406
 
3.9%
dresden 7522
 
2.8%
magdeburg 4860
 
1.8%
halle_saale 4565
 
1.7%
münchen 4383
 
1.6%
essen 4351
 
1.6%
frankfurt_am_main 4296
 
1.6%
hamburg 3759
 
1.4%
Other values (409) 198410
73.8%
2024-04-03T20:30:11.202149image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3174626
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3174626
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3174626
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 424715
 
13.4%
r 301685
 
9.5%
i 274078
 
8.6%
s 227570
 
7.2%
n 210371
 
6.6%
_ 166249
 
5.2%
a 160276
 
5.0%
K 116521
 
3.7%
l 106367
 
3.4%
t 95234
 
3.0%
Other values (43) 1091560
34.4%

regio3
Text

Distinct8684
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:11.462404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length54
Median length39
Mean length11.027699
Min length2

Characters and Unicode

Total characters2964797
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1533 ?
Unique (%)0.6%

Sample

1st rowSchüren
2nd rowBöhl_Iggelheim
3rd rowÄußere_Neustadt_Antonstadt
4th rowFreiberg
5th rowNeu_Schwachhausen
ValueCountFrequency (%)
innenstadt 4751
 
1.8%
stadtmitte 2697
 
1.0%
altstadt 2334
 
0.9%
sonnenberg 1913
 
0.7%
kaßberg 1712
 
0.6%
mitte 1611
 
0.6%
hilbersdorf 1297
 
0.5%
schloßchemnitz 1266
 
0.5%
zentrum 1135
 
0.4%
südstadt 1117
 
0.4%
Other values (8674) 249017
92.6%
2024-04-03T20:30:12.801734image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 384148
 
13.0%
n 225388
 
7.6%
r 219184
 
7.4%
t 212048
 
7.2%
a 177931
 
6.0%
i 145578
 
4.9%
s 137404
 
4.6%
d 137338
 
4.6%
l 123494
 
4.2%
h 117855
 
4.0%
Other values (59) 1084429
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2964797
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 384148
 
13.0%
n 225388
 
7.6%
r 219184
 
7.4%
t 212048
 
7.2%
a 177931
 
6.0%
i 145578
 
4.9%
s 137404
 
4.6%
d 137338
 
4.6%
l 123494
 
4.2%
h 117855
 
4.0%
Other values (59) 1084429
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2964797
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 384148
 
13.0%
n 225388
 
7.6%
r 219184
 
7.4%
t 212048
 
7.2%
a 177931
 
6.0%
i 145578
 
4.9%
s 137404
 
4.6%
d 137338
 
4.6%
l 123494
 
4.2%
h 117855
 
4.0%
Other values (59) 1084429
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2964797
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 384148
 
13.0%
n 225388
 
7.6%
r 219184
 
7.4%
t 212048
 
7.2%
a 177931
 
6.0%
i 145578
 
4.9%
s 137404
 
4.6%
d 137338
 
4.6%
l 123494
 
4.2%
h 117855
 
4.0%
Other values (59) 1084429
36.6%

description
Text

MISSING 

Distinct212621
Distinct (%)85.4%
Missing19747
Missing (%)7.3%
Memory size2.1 MiB
2024-04-03T20:30:22.574334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3949
Median length2701
Mean length510.72708
Min length1

Characters and Unicode

Total characters127223649
Distinct characters337
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique197325 ?
Unique (%)79.2%

Sample

1st rowDie ebenerdig zu erreichende Erdgeschosswohnung befindet sich in einem gepflegten 8-Familienhaus. Aufgrund der Hanglage bietet sich ein unverbaubarer Blick ins Grüne.
2nd rowAlles neu macht der Mai – so kann es auch für Sie in 2019 sein! Genießen Sie das „reine“ Gefühl und die „Unberührtheit“, die diese Wohnung nach der Kernsanierung bietet. Sie verfügt über eine Wohnfläche von ca. 89 m² und einen äußerst gelungenen Grundriss. Aufgeteilt ist die Wohnung in einen großzügigen Wohn-Essbereich, eine Küche, ein Schlafzimmer, ein Kinder- oder Arbeitszimmer, ein Bad, ein Gäste-WC und einen Flur. Von der Küche aus haben Sie direkten Zugang zum Balkon, der zum gemütlichen Verweilen und Entspannen einlädt. Das Badezimmer ist ausgestattet mit Dusche, Toilette, Waschbecken und praktischem Handtuchheizkörper. Zudem gibt es hier jeweils einen Anschluss für die Waschmaschine und für den Trockner. Sämtliche Räume in der Wohnung sind lichtdurchflutet, freundlich und einladend, verstärkt durch die weißen, doppelt verglasten Alufenster und die weißen Wände. Die Böden sind mit pflegeleichtem Vinyl-Boden und Fliesen ausgelegt und somit auch für Allergiker geeignet. Beheizt wird die Wohnung mittels einer neuen, energieeffizienten Gas-Etagenheizung der Firma Vaillant. Im Keller steht allen Mietern ein gemeinschaftlicher Raum zur Verfügung, der für zusätzlichen Stauraum sorgt. Abgerundet wird dieses tolle Angebot durch 2 Stellplätze, auf dem Sie Ihre Fahrzeuge stets sicher und ohne Parkplatzsuche parken können. Wir werden uns bemühen Ihre Anfrage so rasch als möglich zu beantworten, bitte haben Sie jedoch Verständnis, wenn dies 1-2 Werktage in Anspruch nehmen kann!
3rd rowDer Neubau entsteht im Herzen der Dresdner Neustadt. Das Baugrundstück befindet sich inmitten einer sehr gefragten Lage. Nicht nur die zentrale Lage und die schnelle öffentliche Verkehrsanbindung durch den zu Fuß erreichbaren Bahnhof, wie auch Nahverkehrsanbindung, sondern auch die Architektur werden diesen Neubaukomplex zu einem weiteren Highlight am Dresdner Wohnungsmarkt machen. Hier entstehen 2- bis 4-Raum Wohnungen mit Wohnflächen zwischen 43 m² und 124 m². Jede Wohnung verfügt über eine Terrasse oder einen Balkon, die Erdgeschosswohnungen erhalten zusätzlich einen Gartenanteil. Die Räumlichkeiten bieten großzügig durchdachte, lichtdurchflutete Räume mit effektiv geschnittenen Grundrissen.
4th rowAbseits von Lärm und Abgasen in Ihre neue Wohnung Ohne Stress, ohne Sorgen, schlüsselfertig einziehen. Das muss kein Traum bleiben.
5th rowEs handelt sich hier um ein saniertes Mehrfamilienhaus aus dem Jahr 1950.
ValueCountFrequency (%)
und 587100
 
3.4%
die 475626
 
2.7%
mit 393307
 
2.3%
in 360349
 
2.1%
der 327669
 
1.9%
ein 275617
 
1.6%
wohnung 270491
 
1.6%
im 240631
 
1.4%
sich 226281
 
1.3%
216301
 
1.2%
Other values (219136) 14017550
80.6%
2024-04-03T20:30:23.282095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16788864
 
13.2%
e 16544271
 
13.0%
n 10349966
 
8.1%
i 8018359
 
6.3%
r 6682478
 
5.3%
t 6041225
 
4.7%
s 5322609
 
4.2%
a 5106980
 
4.0%
h 4587364
 
3.6%
u 4384723
 
3.4%
Other values (327) 43396810
34.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127223649
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16788864
 
13.2%
e 16544271
 
13.0%
n 10349966
 
8.1%
i 8018359
 
6.3%
r 6682478
 
5.3%
t 6041225
 
4.7%
s 5322609
 
4.2%
a 5106980
 
4.0%
h 4587364
 
3.6%
u 4384723
 
3.4%
Other values (327) 43396810
34.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127223649
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16788864
 
13.2%
e 16544271
 
13.0%
n 10349966
 
8.1%
i 8018359
 
6.3%
r 6682478
 
5.3%
t 6041225
 
4.7%
s 5322609
 
4.2%
a 5106980
 
4.0%
h 4587364
 
3.6%
u 4384723
 
3.4%
Other values (327) 43396810
34.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127223649
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16788864
 
13.2%
e 16544271
 
13.0%
n 10349966
 
8.1%
i 8018359
 
6.3%
r 6682478
 
5.3%
t 6041225
 
4.7%
s 5322609
 
4.2%
a 5106980
 
4.0%
h 4587364
 
3.6%
u 4384723
 
3.4%
Other values (327) 43396810
34.1%

facilities
Text

MISSING 

Distinct189526
Distinct (%)87.8%
Missing52924
Missing (%)19.7%
Memory size2.1 MiB
2024-04-03T20:30:34.486913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3923
Median length2293
Mean length336.39852
Min length1

Characters and Unicode

Total characters72637186
Distinct characters271
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176510 ?
Unique (%)81.7%

Sample

1st rowDie Wohnung ist mit Laminat ausgelegt. Das Badezimmer ist gefliest und verfügt über eine Wannendusche. Neue weiße Zimmertüren, ein Fliesenspiegel in der Küche und Fußleisten wurden kürzlich eingebaut. Zur Wohnung gehört ein 10 m großer Keller. Eine Garage kann optional mitgemietet werden.
2nd row* 9 m² Balkon * Bad mit bodengleicher Dusche, Badewanne und Fenster * Gäste-WC * Waschmaschinenanschluss im Bad und im Waschkeller * Abstell * Fußbodenheizung * Fliesen & Echtholzparkett * elektrische Rollläden * Videotürsprechanlage mit Farbdisplay * Aufzug * KfW-Effizienshaus 55 * Tiefgaragenstellplatz (Miete bereits in der Gesamtmiete enthalten) ~ Der Mietbeginn: ca. Anfang 2020 ~ Baustelle: Betreten verboten! Besichtigungen noch nicht möglich!
3rd rowDiese Wohnung wurde neu saniert und ist wie folgt ausgestattet: - 3 geräumige Zimmer - Wohnzimmer mit optisch getrenntem Ess-,od. Arbeitsbereich - hochwertiger Vinylbodenbelag in allen Wohnräumen sowie im Flur - große Terrasse, hofseitig und mit Blick ins Grüne - Terrasse mit Markise , elektisch Bedienbar - neu und modern geflieste Küche mit Zugang zur Terrasse - kleine Speisekammer - neu und modern gefliestes Badezimmer mit Badewanne incl. Duschtrennwand Je nach Kapazität steht dem Mieter ein Keller zur Verfügung. Eine Garage kann optional angemietet werden.
4th rowhelle ebenerdige 2 Zi. Wohnung mit Terrasse, helles Duschbad, helle EBK, Waschmaschinenanschluss Kaltmiete 315,20€ Nebenkostenvorauszahlung 62,00€ Heizkostenvorauszahlung 80,00€ = gesamt Warmmiete 457,20€
5th rowRollläden; Warmwasserbereiter; Kellerraum; Gas-Zentralheizung; Rauchwarnmelder; Fenster mit Wärmeschutzverglasung; Bodenbelag PVC; Sat-Anlage/Kabel; Balkon;
ValueCountFrequency (%)
607609
 
6.4%
mit 368858
 
3.9%
und 366655
 
3.8%
die 215142
 
2.3%
in 167422
 
1.8%
der 143620
 
1.5%
wohnung 132585
 
1.4%
ist 116664
 
1.2%
im 112114
 
1.2%
bad 107481
 
1.1%
Other values (130305) 7209839
75.5%
2024-04-03T20:30:35.345019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 9002912
 
12.4%
8834662
 
12.2%
n 5505400
 
7.6%
i 4115417
 
5.7%
r 3695621
 
5.1%
t 3431460
 
4.7%
a 3339873
 
4.6%
s 3073988
 
4.2%
h 2497437
 
3.4%
l 2455435
 
3.4%
Other values (261) 26684981
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72637186
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 9002912
 
12.4%
8834662
 
12.2%
n 5505400
 
7.6%
i 4115417
 
5.7%
r 3695621
 
5.1%
t 3431460
 
4.7%
a 3339873
 
4.6%
s 3073988
 
4.2%
h 2497437
 
3.4%
l 2455435
 
3.4%
Other values (261) 26684981
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72637186
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 9002912
 
12.4%
8834662
 
12.2%
n 5505400
 
7.6%
i 4115417
 
5.7%
r 3695621
 
5.1%
t 3431460
 
4.7%
a 3339873
 
4.6%
s 3073988
 
4.2%
h 2497437
 
3.4%
l 2455435
 
3.4%
Other values (261) 26684981
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72637186
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 9002912
 
12.4%
8834662
 
12.2%
n 5505400
 
7.6%
i 4115417
 
5.7%
r 3695621
 
5.1%
t 3431460
 
4.7%
a 3339873
 
4.6%
s 3073988
 
4.2%
h 2497437
 
3.4%
l 2455435
 
3.4%
Other values (261) 26684981
36.7%

heatingCosts
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct5669
Distinct (%)6.6%
Missing183332
Missing (%)68.2%
Infinite0
Infinite (%)0.0%
Mean76.990866
Minimum0
Maximum12613
Zeros2754
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:35.658141image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q154
median70
Q390
95-th percentile138
Maximum12613
Range12613
Interquartile range (IQR)36

Descriptive statistics

Standard deviation147.71628
Coefficient of variation (CV)1.9186208
Kurtosis4563.7772
Mean76.990866
Median Absolute Deviation (MAD)20
Skewness63.887361
Sum6584104.9
Variance21820.099
MonotonicityNot monotonic
2024-04-03T20:30:35.982607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 5310
 
2.0%
70 4811
 
1.8%
80 4763
 
1.8%
50 4286
 
1.6%
100 3970
 
1.5%
90 3015
 
1.1%
0 2754
 
1.0%
75 2601
 
1.0%
65 2588
 
1.0%
40 2088
 
0.8%
Other values (5659) 49332
 
18.3%
(Missing) 183332
68.2%
ValueCountFrequency (%)
0 2754
1.0%
1 21
 
< 0.1%
1.2 1
 
< 0.1%
1.5 3
 
< 0.1%
2 3
 
< 0.1%
3 7
 
< 0.1%
3.22 1
 
< 0.1%
4 5
 
< 0.1%
4.84 1
 
< 0.1%
5 4
 
< 0.1%
ValueCountFrequency (%)
12613 1
< 0.1%
12043 1
< 0.1%
11908 1
< 0.1%
11786 1
< 0.1%
11754 1
< 0.1%
11461 1
< 0.1%
10767 1
< 0.1%
10223 1
< 0.1%
9452 1
< 0.1%
9016 2
< 0.1%

energyEfficiencyClass
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing191063
Missing (%)71.1%
Memory size2.1 MiB
C
14613 
NO_INFORMATION
14130 
D
13925 
B
11333 
E
7987 
Other values (5)
15799 

Length

Max length14
Median length1
Mean length3.5940067
Min length1

Characters and Unicode

Total characters279567
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowE
3rd rowNO_INFORMATION
4th rowE
5th rowC

Common Values

ValueCountFrequency (%)
C 14613
 
5.4%
NO_INFORMATION 14130
 
5.3%
D 13925
 
5.2%
B 11333
 
4.2%
E 7987
 
3.0%
A 4988
 
1.9%
F 4484
 
1.7%
A_PLUS 3618
 
1.3%
G 1806
 
0.7%
H 903
 
0.3%
(Missing) 191063
71.1%

Length

2024-04-03T20:30:36.262506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:36.522463image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
c 14613
18.8%
no_information 14130
18.2%
d 13925
17.9%
b 11333
14.6%
e 7987
10.3%
a 4988
 
6.4%
f 4484
 
5.8%
a_plus 3618
 
4.7%
g 1806
 
2.3%
h 903
 
1.2%

Most occurring characters

ValueCountFrequency (%)
O 42390
15.2%
N 42390
15.2%
I 28260
10.1%
A 22736
8.1%
F 18614
 
6.7%
_ 17748
 
6.3%
C 14613
 
5.2%
T 14130
 
5.1%
M 14130
 
5.1%
R 14130
 
5.1%
Other values (9) 50426
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 279567
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 42390
15.2%
N 42390
15.2%
I 28260
10.1%
A 22736
8.1%
F 18614
 
6.7%
_ 17748
 
6.3%
C 14613
 
5.2%
T 14130
 
5.1%
M 14130
 
5.1%
R 14130
 
5.1%
Other values (9) 50426
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 279567
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 42390
15.2%
N 42390
15.2%
I 28260
10.1%
A 22736
8.1%
F 18614
 
6.7%
_ 17748
 
6.3%
C 14613
 
5.2%
T 14130
 
5.1%
M 14130
 
5.1%
R 14130
 
5.1%
Other values (9) 50426
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 279567
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 42390
15.2%
N 42390
15.2%
I 28260
10.1%
A 22736
8.1%
F 18614
 
6.7%
_ 17748
 
6.3%
C 14613
 
5.2%
T 14130
 
5.1%
M 14130
 
5.1%
R 14130
 
5.1%
Other values (9) 50426
18.0%

lastRefurbish
Real number (ℝ)

MISSING  SKEWED 

Distinct88
Distinct (%)0.1%
Missing188139
Missing (%)70.0%
Infinite0
Infinite (%)0.0%
Mean2013.9045
Minimum1015
Maximum2919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:37.056178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1015
5-th percentile1997
Q12012
median2017
Q32019
95-th percentile2019
Maximum2919
Range1904
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.963125
Coefficient of variation (CV)0.0054437162
Kurtosis3718.1843
Mean2013.9045
Median Absolute Deviation (MAD)2
Skewness24.624326
Sum1.6254425 × 108
Variance120.19011
MonotonicityNot monotonic
2024-04-03T20:30:37.436908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 20202
 
7.5%
2018 13206
 
4.9%
2017 7111
 
2.6%
2016 5679
 
2.1%
2015 4638
 
1.7%
2020 3650
 
1.4%
2014 3468
 
1.3%
2013 2465
 
0.9%
2012 2283
 
0.8%
2010 1836
 
0.7%
Other values (78) 16173
 
6.0%
(Missing) 188139
70.0%
ValueCountFrequency (%)
1015 1
 
< 0.1%
1867 1
 
< 0.1%
1893 1
 
< 0.1%
1898 1
 
< 0.1%
1900 5
< 0.1%
1903 2
 
< 0.1%
1905 1
 
< 0.1%
1910 1
 
< 0.1%
1914 1
 
< 0.1%
1918 1
 
< 0.1%
ValueCountFrequency (%)
2919 2
 
< 0.1%
2918 1
 
< 0.1%
2917 1
 
< 0.1%
2916 1
 
< 0.1%
2145 1
 
< 0.1%
2118 1
 
< 0.1%
2115 1
 
< 0.1%
2107 2
 
< 0.1%
2021 3
 
< 0.1%
2020 3650
1.4%

electricityBasePrice
Categorical

IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing222004
Missing (%)82.6%
Memory size2.1 MiB
90.76
42856 
71.43
 
3990

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters234230
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row90.76
2nd row90.76
3rd row90.76
4th row90.76
5th row90.76

Common Values

ValueCountFrequency (%)
90.76 42856
 
15.9%
71.43 3990
 
1.5%
(Missing) 222004
82.6%

Length

2024-04-03T20:30:37.748581image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:37.918233image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
90.76 42856
91.5%
71.43 3990
 
8.5%

Most occurring characters

ValueCountFrequency (%)
. 46846
20.0%
7 46846
20.0%
9 42856
18.3%
0 42856
18.3%
6 42856
18.3%
1 3990
 
1.7%
4 3990
 
1.7%
3 3990
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 234230
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 46846
20.0%
7 46846
20.0%
9 42856
18.3%
0 42856
18.3%
6 42856
18.3%
1 3990
 
1.7%
4 3990
 
1.7%
3 3990
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 234230
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 46846
20.0%
7 46846
20.0%
9 42856
18.3%
0 42856
18.3%
6 42856
18.3%
1 3990
 
1.7%
4 3990
 
1.7%
3 3990
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 234230
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 46846
20.0%
7 46846
20.0%
9 42856
18.3%
0 42856
18.3%
6 42856
18.3%
1 3990
 
1.7%
4 3990
 
1.7%
3 3990
 
1.7%

electricityKwhPrice
Real number (ℝ)

MISSING 

Distinct15
Distinct (%)< 0.1%
Missing222004
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean0.19976928
Minimum0.1705
Maximum0.2276
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-04-03T20:30:38.092545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.1705
5-th percentile0.1845
Q10.1915
median0.1985
Q30.2055
95-th percentile0.2195
Maximum0.2276
Range0.0571
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.0096670843
Coefficient of variation (CV)0.048391246
Kurtosis1.2431786
Mean0.19976928
Median Absolute Deviation (MAD)0.007
Skewness0.46515851
Sum9358.3916
Variance9.3452519 × 10-5
MonotonicityNot monotonic
2024-04-03T20:30:38.338142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.1985 14639
 
5.4%
0.2055 13076
 
4.9%
0.1915 10114
 
3.8%
0.1845 2952
 
1.1%
0.2276 1411
 
0.5%
0.2125 727
 
0.3%
0.2074 705
 
0.3%
0.1775 676
 
0.3%
0.2205 662
 
0.2%
0.2137 450
 
0.2%
Other values (5) 1434
 
0.5%
(Missing) 222004
82.6%
ValueCountFrequency (%)
0.1705 240
 
0.1%
0.1775 676
 
0.3%
0.1845 2952
 
1.1%
0.1915 10114
3.8%
0.1985 14639
5.4%
0.2055 13076
4.9%
0.2074 705
 
0.3%
0.2125 727
 
0.3%
0.2132 404
 
0.2%
0.2137 450
 
0.2%
ValueCountFrequency (%)
0.2276 1411
 
0.5%
0.2265 193
 
0.1%
0.2205 662
 
0.2%
0.2195 239
 
0.1%
0.2144 358
 
0.1%
0.2137 450
 
0.2%
0.2132 404
 
0.2%
0.2125 727
 
0.3%
0.2074 705
 
0.3%
0.2055 13076
4.9%

date
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Feb20
79276 
May19
76047 
Oct19
66685 
Sep18
46842 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters1344250
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMay19
2nd rowMay19
3rd rowOct19
4th rowMay19
5th rowFeb20

Common Values

ValueCountFrequency (%)
Feb20 79276
29.5%
May19 76047
28.3%
Oct19 66685
24.8%
Sep18 46842
17.4%

Length

2024-04-03T20:30:38.526075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-03T20:30:38.713813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
feb20 79276
29.5%
may19 76047
28.3%
oct19 66685
24.8%
sep18 46842
17.4%

Most occurring characters

ValueCountFrequency (%)
1 189574
14.1%
9 142732
10.6%
e 126118
 
9.4%
F 79276
 
5.9%
b 79276
 
5.9%
2 79276
 
5.9%
0 79276
 
5.9%
M 76047
 
5.7%
a 76047
 
5.7%
y 76047
 
5.7%
Other values (6) 340581
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1344250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 189574
14.1%
9 142732
10.6%
e 126118
 
9.4%
F 79276
 
5.9%
b 79276
 
5.9%
2 79276
 
5.9%
0 79276
 
5.9%
M 76047
 
5.7%
a 76047
 
5.7%
y 76047
 
5.7%
Other values (6) 340581
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1344250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 189574
14.1%
9 142732
10.6%
e 126118
 
9.4%
F 79276
 
5.9%
b 79276
 
5.9%
2 79276
 
5.9%
0 79276
 
5.9%
M 76047
 
5.7%
a 76047
 
5.7%
y 76047
 
5.7%
Other values (6) 340581
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1344250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 189574
14.1%
9 142732
10.6%
e 126118
 
9.4%
F 79276
 
5.9%
b 79276
 
5.9%
2 79276
 
5.9%
0 79276
 
5.9%
M 76047
 
5.7%
a 76047
 
5.7%
y 76047
 
5.7%
Other values (6) 340581
25.3%

Interactions

2024-04-03T20:29:37.550201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:27:57.681993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:04.745965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:09.312089image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:19.615301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:23.916686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:29.199426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:34.156859image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:38.653787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:44.564234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:48.829177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:52.958619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:57.369682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:02.201787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:06.493098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:10.953928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:15.917650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:19.942900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:23.872467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:29.184952image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:33.283745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:37.724292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:27:59.402861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:05.008373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:09.536342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:19.827899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:24.169628image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:29.412696image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:34.387676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:39.195137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:44.807642image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:49.056638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:53.187889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:57.645633image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:02.428504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:06.693829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:11.274514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:16.119658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:20.153347image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:24.082271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:29.433078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:33.459486image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:37.893816image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:27:59.619259image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:05.241317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:09.755982image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:20.094275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:24.418447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:29.727578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:34.597436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:39.585078image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:45.026177image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:49.264459image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:53.394065image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:57.914535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:02.682573image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:06.921292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:11.526722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:16.311320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:20.339383image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:24.294981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:29.661728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:33.642053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:38.070631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:27:59.857033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:05.458639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:10.052993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:20.320174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:24.652103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:29.976861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:34.799344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:39.868556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:45.221964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:49.449191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:53.583030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:58.164290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:02.878232image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:07.180814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:11.772627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:16.492352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:20.536708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:24.489820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:29.896946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:33.810137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:38.249359image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:00.093618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:05.653153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:10.331052image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:20.522187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:24.899354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:30.229756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:35.008965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:40.136567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:45.428897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:49.637186image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:53.771344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:58.396794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:03.087920image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:07.470203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:12.062273image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:16.680681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:20.720550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:24.690426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:30.088989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:33.966714image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:38.425921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:00.337043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:05.906667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:10.585153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:20.749454image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:25.159103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-04-03T20:29:27.345553image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:31.953638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:35.697249image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:40.219843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:03.404063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:08.166674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:18.322744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:22.770515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:27.705154image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:32.992115image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:37.454643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:43.425743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:47.695466image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:51.840061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:55.876071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:00.935659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:05.313130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:09.726780image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:14.420942image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:18.833234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:22.753030image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:27.644171image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:32.146391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:35.880515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:40.377620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:03.621762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:08.370431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:18.515036image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:22.964747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:28.215572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:33.210599image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:37.666234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:43.610438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:47.892220image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:52.023936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:56.054070image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:01.149601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:05.494742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:09.902570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:14.576401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:19.027355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:22.936438image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:27.938014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:32.325312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:36.061825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:40.558744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:03.841019image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:08.581074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:18.756411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:23.177603image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:28.436181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:33.434756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:37.907794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:43.795494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:48.099169image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:52.225312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:56.248796image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:01.386701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:05.696794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:10.096493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:14.770307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:19.233237image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:23.147263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:28.215332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:32.546505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:36.248594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:40.743450image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:04.030632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:08.751706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:18.992292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:23.348435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:28.615654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:33.602705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:38.096787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:43.973298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:48.279139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:52.384055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:56.739733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:01.569168image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:05.872750image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:10.255524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:14.962755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:19.398185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:23.315608image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:28.434868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:32.741812image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:36.444705image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:40.943467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:04.263861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:08.926607image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:19.208754image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:23.529090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:28.789164image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:33.777647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:38.276139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:44.140975image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:48.445787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:52.573243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:56.904197image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:01.749158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:06.049518image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:10.410393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:15.137978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:19.577325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:23.485215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:28.660327image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:32.958594image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:36.614817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:41.135039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:04.495374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:09.101158image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:19.396927image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:23.692433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:28.972842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:33.941887image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:38.463530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:44.341088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:48.600557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:52.759135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:28:57.136016image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:01.954619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:06.258555image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:10.654048image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:15.682601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:19.751933image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:23.650811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:28.928073image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:33.113514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-03T20:29:36.834372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-03T20:29:41.986236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-03T20:29:44.099460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

regio1serviceChargeheatingTypetelekomTvOffertelekomHybridUploadSpeednewlyConstbalconypicturecountpricetrendtelekomUploadSpeedtotalRentyearConstructedscoutIdnoParkSpacesfiringTypeshasKitchengeo_blncellaryearConstructedRangebaseRenthouseNumberlivingSpacegeo_krsconditioninteriorQualpetsAllowedstreetstreetPlainliftbaseRentRangetypeOfFlatgeo_plznoRoomsthermalCharfloornumberOfFloorsnoRoomsRangegardenlivingSpaceRangeregio2regio3descriptionfacilitiesheatingCostsenergyEfficiencyClasslastRefurbishelectricityBasePriceelectricityKwhPricedate
0Nordrhein_Westfalen245.00central_heatingONE_YEAR_FREENaNFalseFalse64.6210.0840.001965.0961070571.0oilFalseNordrhein_WestfalenTrue2.0595.0024486.00Dortmundwell_keptnormalNaNSch&uuml;ruferstra&szlig;eSchüruferstraßeFalse4ground_floor442694.0181.41.03.04True4DortmundSchürenDie ebenerdig zu erreichende Erdgeschosswohnung befindet sich in einem gepflegten 8-Familienhaus. Aufgrund der Hanglage bietet sich ein unverbaubarer Blick ins Grüne.Die Wohnung ist mit Laminat ausgelegt. Das Badezimmer ist gefliest und verfügt über eine Wannendusche. Neue weiße Zimmertüren, ein Fliesenspiegel in der Küche und Fußleisten wurden kürzlich eingebaut.\nZur Wohnung gehört ein 10 m großer Keller. Eine Garage kann optional mitgemietet werden.NaNNaNNaNNaNNaNMay19
1Rheinland_Pfalz134.00self_contained_central_heatingONE_YEAR_FREENaNFalseTrue83.4710.0NaN1871.01113787342.0gasFalseRheinland_PfalzFalse1.0800.00NaN89.00Rhein_Pfalz_Kreisrefurbishednormalnono_informationNaNFalse5ground_floor674593.0NaNNaNNaN3False4Rhein_Pfalz_KreisBöhl_IggelheimAlles neu macht der Mai – so kann es auch für Sie in 2019 sein! Genießen Sie das „reine“ Gefühl und die „Unberührtheit“, die diese Wohnung nach der Kernsanierung bietet.\n\nSie verfügt über eine Wohnfläche von ca. 89 m² und einen äußerst gelungenen Grundriss.\n\nAufgeteilt ist die Wohnung in einen großzügigen Wohn-Essbereich, eine Küche, ein Schlafzimmer, ein Kinder- oder Arbeitszimmer, ein Bad, ein Gäste-WC und einen Flur.\n\nVon der Küche aus haben Sie direkten Zugang zum Balkon, der zum gemütlichen Verweilen und Entspannen einlädt.\n\nDas Badezimmer ist ausgestattet mit Dusche, Toilette, Waschbecken und praktischem Handtuchheizkörper. Zudem gibt es hier jeweils einen Anschluss für die Waschmaschine und für den Trockner.\n\nSämtliche Räume in der Wohnung sind lichtdurchflutet, freundlich und einladend, verstärkt durch die weißen, doppelt verglasten Alufenster und die weißen Wände. \n\nDie Böden sind mit pflegeleichtem Vinyl-Boden und Fliesen ausgelegt und somit auch für Allergiker geeignet.\n\nBeheizt wird die Wohnung mittels einer neuen, energieeffizienten Gas-Etagenheizung der Firma Vaillant. \n\nIm Keller steht allen Mietern ein gemeinschaftlicher Raum zur Verfügung, der für zusätzlichen Stauraum sorgt.\n\nAbgerundet wird dieses tolle Angebot durch 2 Stellplätze, auf dem Sie Ihre Fahrzeuge stets sicher und ohne Parkplatzsuche parken können.\n\n\nWir werden uns bemühen Ihre Anfrage so rasch als möglich zu beantworten, bitte haben Sie jedoch Verständnis, wenn dies 1-2 Werktage in Anspruch nehmen kann!NaNNaNNaN2019.0NaNNaNMay19
2Sachsen255.00floor_heatingONE_YEAR_FREE10.0TrueTrue82.722.41300.002019.01131475231.0NaNFalseSachsenTrue9.0965.00483.80Dresdenfirst_time_usesophisticatedNaNTurnerwegTurnerwegTrue6apartment10973.0NaN3.04.03False4DresdenÄußere_Neustadt_AntonstadtDer Neubau entsteht im Herzen der Dresdner Neustadt.\nDas Baugrundstück befindet sich inmitten einer sehr gefragten Lage.\nNicht nur die zentrale Lage und die schnelle öffentliche\nVerkehrsanbindung durch den zu Fuß erreichbaren Bahnhof, wie auch Nahverkehrsanbindung, sondern auch die Architektur werden diesen\nNeubaukomplex zu einem weiteren Highlight am Dresdner Wohnungsmarkt machen.\nHier entstehen 2- bis 4-Raum Wohnungen mit Wohnflächen zwischen 43 m² und 124 m². Jede Wohnung verfügt über eine Terrasse oder einen Balkon, die Erdgeschosswohnungen erhalten zusätzlich einen Gartenanteil. Die Räumlichkeiten bieten großzügig durchdachte, lichtdurchflutete Räume mit effektiv geschnittenen Grundrissen.* 9 m² Balkon\n* Bad mit bodengleicher Dusche, Badewanne und Fenster\n* Gäste-WC\n* Waschmaschinenanschluss im Bad und im Waschkeller\n* Abstell\n* Fußbodenheizung\n* Fliesen & Echtholzparkett\n* elektrische Rollläden\n* Videotürsprechanlage mit Farbdisplay\n* Aufzug\n* KfW-Effizienshaus 55\n* Tiefgaragenstellplatz (Miete bereits in der Gesamtmiete enthalten)\n\n~ Der Mietbeginn: ca. Anfang 2020\n~ Baustelle: Betreten verboten! Besichtigungen noch nicht möglich!NaNNaNNaNNaNNaNOct19
3Sachsen58.15district_heatingONE_YEAR_FREENaNFalseTrue91.5340.0NaN1964.0108890903NaNdistrict_heatingFalseSachsenFalse2.0343.003558.15Mittelsachsen_KreisNaNNaNNaNGl&uuml;ck-Auf-Stra&szlig;eGlück-Auf-StraßeFalse2other95993.086.03.0NaN3False2Mittelsachsen_KreisFreibergAbseits von Lärm und Abgasen in Ihre neue Wohnung\r\nOhne Stress, ohne Sorgen, schlüsselfertig einziehen.\r\nDas muss kein Traum bleiben.NaN87.23NaNNaNNaNNaNMay19
4Bremen138.00self_contained_central_heatingNaNNaNFalseTrue192.46NaN903.001950.0114751222NaNgasFalseBremenFalse1.0765.001084.97BremenrefurbishedNaNNaNHermann-Henrich-Meier-AlleeHermann-Henrich-Meier-AlleeFalse5apartment282133.0188.91.0NaN3False4BremenNeu_SchwachhausenEs handelt sich hier um ein saniertes Mehrfamilienhaus aus dem Jahr 1950. \n\rDiese Wohnung wurde neu saniert und ist wie folgt ausgestattet:\r\r- 3 geräumige Zimmer\r- Wohnzimmer mit optisch getrenntem Ess-,od. Arbeitsbereich\r- hochwertiger Vinylbodenbelag in allen Wohnräumen sowie im Flur\r- große Terrasse, hofseitig und mit Blick ins Grüne\r- Terrasse mit Markise , elektisch Bedienbar\r- neu und modern geflieste Küche mit Zugang zur Terrasse\r- kleine Speisekammer\r- neu und modern gefliestes Badezimmer mit Badewanne incl. Duschtrennwand\r\rJe nach Kapazität steht dem Mieter ein Keller zur Verfügung. \r\rEine Garage kann optional angemietet werden.\rNaNNaNNaNNaNNaNFeb20
5Schleswig_Holstein142.00gas_heatingNONENaNFalseTrue54.482.4NaN1999.0115531145NaNgasTrueSchleswig_HolsteinFalse5.0315.201e53.43Schleswig_Flensburg_Kreiswell_keptNaNnoHardeseicheHardeseicheFalse2apartment248912.0165.0NaNNaN2False2Schleswig_Flensburg_KreisStruxdorfNaNhelle ebenerdige 2 Zi. Wohnung mit Terrasse, helles Duschbad, helle EBK, Waschmaschinenanschluss\r\nKaltmiete 315,20€ Nebenkostenvorauszahlung 62,00€ Heizkostenvorauszahlung 80,00€ = gesamt Warmmiete 457,20€\r\nNaNNaNNaNNaNNaNFeb20
6Sachsen70.00self_contained_central_heatingONE_YEAR_FREE10.0FalseFalse91.012.4380.00NaN114391930NaNNaNFalseSachsenTrueNaN310.001462.00Mittelsachsen_Kreisfully_renovatedNaNNaNAm BahnhofAm_BahnhofFalse2NaN95992.0NaN1.04.02True3Mittelsachsen_KreisFreibergAm Bahnhof 14 in Freiberg\nHeizkosten und Warmwasseraufbereitung sind direkt an Versorgungsträger zu richten. ca. 70,- Euro monatlich\nNicht in den angegebenen Nebenkosten enthalten (Etagenheizung)\nGroßes Tageslichtbad mit Wanne und Waschmaschinenanschluss\n\nHinweis Energieausweis: Nicht notwendig da Denkmalschutz\nAnbieter-Objektnummer: ABM141OGlNaNNaNNaNNaNNaNNaNFeb20
7Bremen88.00central_heatingONE_YEAR_FREE10.0FalseTrue51.892.4584.251959.0115270775NaNgas:electricityFalseBremenTrue2.0452.253560.30BremenNaNNaNNaNLesumer Heerstr.Lesumer_Heerstr.False3ground_floor287173.063.0NaNNaN3False2BremenSt._Magnus+ Komfortabler Bodenbelag: Die Wohnung ist zusätzlich mit barrierearmen und pflegeleichten Fußbodenbelag ausgestattet.\n\nDiese schöne 3-Zimmer-Wohnung befindet sich in einer sehr gepflegten Wohnanalge in bester Lage von Lesum. Der Balkon lädt Sie zum verweilen ein. Das Tageslichtbad ist mit einer Badewanne ausgestattet, hier können Sie sich nach einem stressigen Tag entspannen. Die Küche ist mit einem Fliesenspiegel ausgestattet und bietetn Platz für Ihre eigene Einbauküche.\n\nWir sind für Sie vor Ort: Mittwochs 14:00 - 17:00 Uhr - Seefahrtstr. 7 in 28759 Bremen - Wir freuen uns auf Ihren Besuch.\n\nRollläden; Warmwasserbereiter; Kellerraum; Gas-Zentralheizung; Rauchwarnmelder; Fenster mit Wärmeschutzverglasung; Bodenbelag PVC; Sat-Anlage/Kabel; Balkon;44.00BNaNNaNNaNFeb20
8Baden_Württemberg110.00oil_heatingONE_YEAR_FREENaNFalseFalse53.7740.0690.001970.01064163611.0oilTrueBaden_WürttembergTrue2.0580.00NaN53.00Emmendingen_Kreiswell_keptsophisticatednono_informationNaNFalse4roof_storey792112.0138.02.02.02False2Emmendingen_KreisDenzlingenDiese ansprechende, lichtdurchflutete DG-Wohnung im zweiten OG besticht durch eine gehobene Innenausstattung. Zu dem Objekt zählen zwei schöne Zimmer. Ein aktueller Energieausweis liegt vor (Dach wurde mit Wärmedämmung ausgestattet). Eine Einbauküche und ein Kellerraum stehen Ihnen außerdem zur Verfügung. Bei Interesse kann eine abschließbare Einzelgarage hinzugemietet werden (70€ / Monat).Parkett, Einbauküche, kein BalkonNaNENaNNaNNaNFeb20
9Nordrhein_Westfalen95.00self_contained_central_heatingONE_YEAR_FREENaNFalseFalse71.9240.0NaN1953.091383597NaNgasFalseNordrhein_WestfalenTrue2.0300.003060.00Gelsenkirchenwell_keptnormalnegotiableH&uuml;ttenstr.Hüttenstr.False1apartment458882.5207.72.05.02False2GelsenkirchenBulmke_HüllenSie sind auf der Suche nach einer gepflegten und günstigen 2,5 Raum - Etagenwohnung in einer zentrumsnahen Lage?\n\nHier ist Sie.\n\nSie setzt sich aus einem Wohnzimmer, einer Küche mit Abstellraum und einem Schlafzimmer nebst Badezimmer zusammen, die alle von der Diele aus begehbar sind. \n\nVor allem das Wohnzimmer überzeugt durch seine ansprechende Größe und bietet reichlich Platz für Ihr Mobiliar.\n\nDie Küche bietet genügend Platz für eine Einbauküche und einen kleinen Esstisch und verfügt über einen praktischen Abstellschrank.\n\nDas Schlafzimmer erlaubt das Aufstellen von einem Kleiderschrank und einem Bett.\n\nDas Badezimmer mit Fenster befindet sich in einem gepflegten Zustand und verfügt über eine Badewanne, einem Waschbecken und einem WC.\n\nAufgrund großer Fenster macht die Wohnung insgesamt einen sehr hellen und freundlichen Eindruck.In Ihrem neuen Zuhause können Sie nach wenigen Handgriffen einziehen.\n\nDie Wände sind mit den Tapeten des Vormieters versehen und müssten gegebenenfalls erneuert werden.\n\nDas Wohnzimmer und die Diele sind am Boden mit einem Laminat ausgestattet.\n\nDie Küche ist am Boden gefliest. Ebenfalls befindet sich in der Küche ein Fliesenspiegel.\n\nZur Zeit befindet sich im Schlafzimmer ein Teppichboden.\n\nDas moderne und anschauliche Tageslichtbad ist deckenhoch an den Wänden gefliest. Am Boden befinden sich ebenfalls Fliesen. Es ist mit einer Badewanne, einem Waschbecken und einem WC ausgestattet.\n\nAn der Decke befinden sich Holz Paneele, in welche Halogen Spoots eingelassen wurden.\n\nDie Wohnung wird mittels einer Gas-Etagenheizung beheizt.\n\nDie Warmwasseraufbereitung erfolgt kostengünstig über die Heizungsanlage.\n\nAuf dem Dachboden besteht die Möglichkeit Wäsche zu trocknen.NaNNaNNaNNaNNaNOct19
regio1serviceChargeheatingTypetelekomTvOffertelekomHybridUploadSpeednewlyConstbalconypicturecountpricetrendtelekomUploadSpeedtotalRentyearConstructedscoutIdnoParkSpacesfiringTypeshasKitchengeo_blncellaryearConstructedRangebaseRenthouseNumberlivingSpacegeo_krsconditioninteriorQualpetsAllowedstreetstreetPlainliftbaseRentRangetypeOfFlatgeo_plznoRoomsthermalCharfloornumberOfFloorsnoRoomsRangegardenlivingSpaceRangeregio2regio3descriptionfacilitiesheatingCostsenergyEfficiencyClasslastRefurbishelectricityBasePriceelectricityKwhPricedate
268840Hessen112.13district_heatingONE_YEAR_FREE10.0FalseTrue146.902.41479.642016.01069954891.0district_heatingTrueHessenTrue9.01255.382089.67Frankfurt_am_Mainmint_conditionsophisticatedyesGundelandst.Gundelandst.True7apartment604353.022.452.04.03True4Frankfurt_am_MainPreungesheimHell und luftig präsentiert sich der fünfgeschossige Bau in dem neu entwickelten „Apfel-Quartier“ von Frankfurt-Preungesheim. Die 130 Mietwohnungen und drei Townhouses, die von attraktiven Ladenflächen im Erdgeschoss ergänzt werden, bilden eine geschlossene und doch einladende Einheit. Der stimmige Entwurf stammt aus der Feder des Architekturbüros Landes & Partner, das wegen seines hohen Anspruchs an die Ästhetik und Funktionalität seiner Objekte ein hohes Renommee genießt.Neben der attraktiven Optik überzeugen auch die inneren Werte der Gravensteiner Arkaden. Das KfW-Effizienzhaus punktet unter anderem mit dreifach verglasten Fenstern und einer erfreulich sparsamen Fußbodenheizung. Auch bei der weiteren Ausstattung wurde größter Wert auf Qualität gelegt: Echtholzparkett im Wohn-Ess-Bereich, Markenprodukte von Grohe in den Bädern und ein außen liegender Sonnenschutz sind nur einige Details, die für das gewisse Extra an Wohlgefühl sorgen. Ein besonderes Highlight ist die voll ausgestattete Küche, die im Mietpreis enthalten ist und mit ihren hochwertigen Miele-Geräten selbst anspruchsvolle Köche begeistert.112.13A_PLUS2016.090.760.1985Sep18
268841Sachsen_Anhalt98.00central_heatingONE_YEAR_FREENaNFalseTrue21.5640.0424.521994.0110721511NaNgas:electricityFalseSachsen_AnhaltTrue5.0302.521157.08MagdeburgNaNNaNNaNThomas-Mann-Str.Thomas-Mann-Str.False2apartment391142.5123.002.0NaN2False2MagdeburgCracauNahe der GETEC-Arena und der Elbe steht dieses gepflegte Hochhaus. Hier wohnen Sie wunderbar ruhig und mit einem schönen Blick ins Grüne. Das Haus verfügt über eine effiziente Wärmedämmung und einen gemütlichen Gemeinschaftsgarten.\n\nTürsprechanlage; Warmwasserbereiter; Kellerraum; Gas-Zentralheizung; Rauchwarnmelder; Bodenbelag PVC; Wärmedämmung der Kellerdecke; Fassadenvollwärmeschutz; Balkon;24.00DNaNNaNNaNMay19
268842Sachsen140.00NaNONE_YEAR_FREENaNFalseFalse70.5440.0440.00NaN1118570411.0NaNTrueSachsenFalseNaN300.006859.89ZwickauNaNNaNNaNM&uuml;hlpfortstra&szlig;eMühlpfortstraße_False1maisonette80582.0NaN3.00.02False2ZwickauNordvorstadtNaNNaN0.00NaNNaNNaNNaNOct19
268843Sachsen120.00central_heatingONE_YEAR_FREENaNFalseTrue122.0040.0368.001930.091110231NaNgasFalseSachsenTrue1.0248.008155.00ChemnitznegotiablesimplenegotiableNeefestra&szlig;eNeefestraßeFalse1apartment91192.0129.001.04.02True2ChemnitzKappelEs handelt sich um ein Mehrfamilienhaus mit 6 Wohneinheiten, erbaut um 1930 und im Jahr 2010 durch den Eigentümer umfassend saniert.- Küche Fliesenfußboden \n- Wohnzimmer / Schlafzimmer Laminat \n- kleines Bad gefliest mit Dusche u. Fenster\n- kleiner Balkon \n- DSL \n- IsofensterNaNNaN2010.090.760.2055Sep18
268844Nordrhein_Westfalen80.00gas_heatingONE_YEAR_FREE10.0FalseFalse122.582.4670.00NaN115526313NaNgasFalseNordrhein_WestfalenFalseNaN590.00NaN85.00Essenfirst_time_use_after_refurbishmentsophisticatednegotiableno_informationNaNFalse4roof_storey452793.0NaN3.03.03False4EssenHorstBei dieser ansprechenden Immobilie handelt es sich um einen Erstbezug nach Sanierung in eine 85m² große helle Dachgeschosswohnung in der dritten Etage, Küche, Diele, Bad, Abstellraum die durch eine gehobene Innenausstattung besticht und ab sofort bezogen werden kann. Das Objekt verfügt über drei attraktive Zimmer. Die letzte Modernisierung fand erst im Jahr 2019 statt.NaNNaNNaN2019.0NaNNaNFeb20
268845Bayern90.00heat_pumpONE_YEAR_FREENaNFalseTrue02.7410.0910.002016.01156410811.0geothermalFalseBayernTrue9.0820.00NaN90.00Weilheim_Schongau_Kreismint_conditionsophisticatednono_informationNaNFalse6roof_storey823903.0NaNNaNNaN3False4Weilheim_Schongau_KreisEberfingDiese schöne, neuwertige Wohnung im Dachgeschoss zeichnet sich durch eine gehobene Innenausstattung und Bergblick aus.\nEin Stellplatz, ein Geräteschuppen, eine Garage und ein Kellerabteil sind zusätzlich vorhanden.Fliesen und Parkett. Sichtbarer Dachstuhl.NaNNaNNaNNaNNaNFeb20
268846Hessen220.00gas_heatingNaNNaNFalseTrue126.49NaN1150.001983.0969814971.0gasTrueHessenFalse4.0930.00NaN115.00Bergstraße_Kreiswell_keptsophisticatednegotiableno_informationNaNFalse6apartment685193.5NaN1.01.03False5Bergstraße_KreisViernheimHier wird eine Wohnung im 2 Familienhaus angeboten.\nDie Wohung liegt in der Nordweststadt.Parkett, Kamin, Badewanne&Dusche\nGroßer Balkon, EinbaukücheNaNNaN2015.0NaNNaNMay19
268847Hessen220.00central_heatingONE_YEAR_FREENaNFalseTrue212.9040.0930.001965.0669242711.0gasFalseHessenTrue2.0650.001095.00Limburg_Weilburg_Kreiswell_keptNaNnegotiableEmsbachstrasseEmsbachstrasseFalse5apartment655524.0160.771.02.04True4Limburg_Weilburg_KreisLimburg_an_der_Lahngemütliche 4-Zimmer-Wohnung im Obergeschoss eines 2-Familienhauses, Wohnzimmer mit Balkonausgang, 2 Schlafräume, Arbeitszimmer oder sep. Esszimmer, Tageslichtbad mit Dusche, sep. WC, Küche, Kellerraum, Waschküche mit Gartenausgang, 770 m² Grundstück, Gartenmitbenutzung, Garage mit Funktor.Böden: Wohn-/Schlafbereich = Laminat, Küche + Flur = Linoleum, Bad = Fliesen. Zentrale SAT-Anlage mit Anschlußmöglichkeiten in allen Zimmern. Das Haus wurde komplett mit einem Vollwärmeschutz versehen. Neue Zentralheizung im Frühjahr 2019 installiert. Die vorhandene Einbauküche mit Geräten kann auf Wunsch vom Vormieter übernommen werden.NaNNaN2019.0NaNNaNFeb20
268848Nordrhein_Westfalen175.00heat_pumpNaNNaNTrueTrue164.39NaN1015.002019.01109383021.0gasFalseNordrhein_WestfalenTrue9.0840.005870.00Kölnfirst_time_usesophisticatednoIdastra&szlig;eIdastraßeTrue6apartment510692.024.70NaN5.02False3KölnDellbrückNeubau Erstbezug, gehobener Standard, alle Einheiten mit Terrasse oder Balkon.\n\nQuadratmeterpreis (kalt): € 12,00\nNebenkosten (warm) je m²: € 2,50\n\n2- und 3-Zimmerwohnungen.\n\nfreie Wohnungsgrößen zwischen 66 und 110 m².\n\nCa. 70% bereits vermietet. Zwei Musterwohnungen können besichtigt werden.\n\nGrundrisse finden Sie unter \nwww.dig.koeln zum download\n\nHeizung/Warmwasser über Wärmepumpe (Luft-Luft), Zugeheizt über Gas, KfW 55 Effizienzhaus\n\nBitte bei Anfragen konkret mitteilen für welche Einheit Sie sich interessieren.\n\nFreie Einheiten können der anliegende Liste entnommen werden.Wände:\nMaler­vlies, weiß gestrichen alter­nativ Raufaser\nBöden:\nParkett Schiffs­boden Eiche und Fein­steinzeug­fliesen 30 x 60 cm\nFußboden­heizung:\nmit Einzel­raum­steuerung\nEnergie­effizienz:\nKfW-Effizienz­haus 55 nach aktu­eller EnEV\nBarriere­reduziert:\nbarriere­freier Zugang zu allen Ein­heiten\nAufzug:\nin allen Geschossen\nErhöhter Schall­schutz:\nim gesamten Objekt\nLichte Raum­höhen:\nzwischen 260 bis 340 cm\nEingangs­türen:\nWK2, drei­fach ver­riegelt\nGegen­sprech­anlage:\nmit Video­kamera\nSanitär­ausstattung:\nDuravit, Modell Stark 3 oder vergleichbar\nArmaturen:\nHans Grohe, Model Talis S oder vergleichbar\nRollläden:\nim EG und nach Wärme­schutz­konzept\nTief­garagentor:\nin der Tief­garage\nFahrräder­abstell­platz:\nin der Tief­garage und vor dem Haus\nAbstell­raum:\nin allen Ein­heiten\nBalkone:\nmit einer Tiefe von 2,00 m40.00NO_INFORMATION2019.0NaNNaNMay19
268849Hessen315.00central_heatingNaNNaNFalseTrue95.00NaNNaN1972.01155240541.0district_heatingTrueHessenTrue3.0935.00877.00Frankfurt_am_MainmodernizedNaNNaNRobert-Di&szlig;mann-Str.Robert-Dißmann-Str.True6apartment659363.0NaNNaN13.03False3Frankfurt_am_MainSossenheimSchöne, helle, gut geschnittene und teilmöblierte Drei-Zimmer-Wohnung in gepflegter Wohnanlage.\nBalkon, Einbauküche und Wannenbad. Schöne Aussicht auf Frankfurter Skyline und Taunus.Balkon, Keller, Fahrstuhl, Vollbad, Einbauküche, Laminat\n\nBemerkungen:\nWohn- und Esszimmer: Couch und Couchtisch(anders als im Bild), Esstisch und Stühle\nKüche: Einbauküche mit Backofen, Kühl- und Gefrierschrank, kochfeld, Dunstabzug\nElternzimmer: Bett 180cmx200cm wie in Bild, Kleiderschrank 250cm breit wie im Bild, zwei Nachttische und zwei Kommoden.\nKinderzimmer: Bett 140cmx 200cm und Schreibtisch und Stuhl.NaNNaNNaNNaNNaNFeb20